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Chauhan R, Varma G, Yafi E, Zuhairi MF. The impact of geo-political socio-economic factors on vaccine dissemination trends: a case-study on COVID-19 vaccination strategies. BMC Public Health 2023; 23:2142. [PMID: 37919737 PMCID: PMC10621224 DOI: 10.1186/s12889-023-17000-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 10/16/2023] [Indexed: 11/04/2023] Open
Abstract
BACKGROUND The world in recent years has seen a pandemic of global scale. To counter the widespread loss of life and severe repercussions, researchers developed vaccinations at a fast pace to immunize the population. While the vaccines were developed and tested through extensive human trials, historically vaccines have been known to evoke mixed sentiments among the generic demographics. In the proposed study, we aim to reveal the impact of political and socio-economic factors on SARS-Cov-2 vaccination trends observed in two hundred and seventeen countries spread across the six continents. METHODS The study had hypothesized that the citizens who have lower trust in their government would be less inclined towards vaccination programs. To test this hypothesis, vaccination trends of nations under authoritarian rule were compared against democratic nations. Further, the study was synthesized with Cov-2 vaccination data which was sourced from Our World Data repository, which was sampled among 217 countries spread across the 6 continents. The study was analyzed with exploratory data analysis and proposed with relevance and impacting factor that was considered for vaccine dissemination in comparison with the literacy rate of the nations. Another impacting factor the study focused on for the vaccination dissemination trends was the health expenses of different nations. The study has been synthesized on political and socio-economic factors where the features were ardently study in retrospect of varied socio- economic features which may include country wise literacy rate, overall GDP rate, further we substantiated the work to address the political factors which are discussed as the country status of democratic or having other status. RESULTS The comparison of trends showed that dissemination of SARS-Cov-2 vaccines had been comparable between the two-opposing types of governance. The major impact factor behind the wide acceptance of the SARS-Cov-2 vaccine was the expenditure done by a country on healthcare. These nations used a large number of vaccines to administer to their population and the trends showed positive growth. The overall percentage of vaccine utilized by countries in quantitative terms are Pfizer/BioNTech (17.55%), Sputnik V (7.08%), Sinovac (6.98%), Sinopharm/Beijing (10.04%), Oxford/AstraZeneca (19.56%), CanSino (2.85%), Moderna (12.05%), Covaxin (3.28%), JohnsonandJohnson (10.89%), Sputnik Light (3.07%), Novavax (3.49%). While the nations with the lowest healthcare expenses failed to keep up with the demand and depended on vaccines donated by other countries to protect their population. CONCLUSIONS The analysis revealed strong indicators that the nations which spend more on healthcare were the ones that had the best SARS-Cov-2 vaccination rollout. To further support decision-making in the future, countries should address the trust and sentiment of their citizens towards vaccination. For this, expenses need to be made to develop and promote vaccines and project them as positive health tools.
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Affiliation(s)
- Ritu Chauhan
- Centre for Computational Biology and Bioinformatics, Amity University, Noida, Uttar Pradesh, India
| | - Gatha Varma
- Amity Institute of Information Technology, Amity University, Noida, Uttar Pradesh, India
| | - Eiad Yafi
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, New South Wales, Australia
| | - Megat F Zuhairi
- UniKL - LR Univ Joint ICT Laboratory (KLR-JIL), Universiti Kuala Lumpur, Malaysia - La Rochelle University, France, Kuala Lumpur, Malaysia.
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2
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Chu WT, Reza SMS, Anibal JT, Landa A, Crozier I, Bağci U, Wood BJ, Solomon J. Artificial Intelligence and Infectious Disease Imaging. J Infect Dis 2023; 228:S322-S336. [PMID: 37788501 PMCID: PMC10547369 DOI: 10.1093/infdis/jiad158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 05/06/2023] [Indexed: 10/05/2023] Open
Abstract
The mass production of the graphics processing unit and the coronavirus disease 2019 (COVID-19) pandemic have provided the means and the motivation, respectively, for rapid developments in artificial intelligence (AI) and medical imaging techniques. This has led to new opportunities to improve patient care but also new challenges that must be overcome before these techniques are put into practice. In particular, early AI models reported high performances but failed to perform as well on new data. However, these mistakes motivated further innovation focused on developing models that were not only accurate but also stable and generalizable to new data. The recent developments in AI in response to the COVID-19 pandemic will reap future dividends by facilitating, expediting, and informing other medical AI applications and educating the broad academic audience on the topic. Furthermore, AI research on imaging animal models of infectious diseases offers a unique problem space that can fill in evidence gaps that exist in clinical infectious disease research. Here, we aim to provide a focused assessment of the AI techniques leveraged in the infectious disease imaging research space, highlight the unique challenges, and discuss burgeoning solutions.
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Affiliation(s)
- Winston T Chu
- Center for Infectious Disease Imaging, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, Maryland, USA
| | - Syed M S Reza
- Center for Infectious Disease Imaging, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
| | - James T Anibal
- Center for Interventional Oncology, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
| | - Adam Landa
- Center for Interventional Oncology, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
| | - Ian Crozier
- Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, Maryland, USA
| | - Ulaş Bağci
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Bradford J Wood
- Center for Interventional Oncology, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Jeffrey Solomon
- Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, Maryland, USA
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3
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Mohammedain SA, Badran S, Elzouki AY, Salim H, Chalaby A, Siddiqui MYA, Hussein YY, Rahim HA, Thalib L, Alam MF, Al-Badriyeh D, Al-Maadeed S, Doi SAR. Validation of a risk prediction model for COVID-19: the PERIL prospective cohort study. Future Virol 2023:10.2217/fvl-2023-0036. [PMID: 37970094 PMCID: PMC10630949 DOI: 10.2217/fvl-2023-0036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 10/03/2023] [Indexed: 11/17/2023]
Abstract
Aim: This study aims to perform an external validation of a recently developed prognostic model for early prediction of the risk of progression to severe COVID-19. Patients & methods/materials: Patients were recruited at their initial diagnosis at two facilities within Hamad Medical Corporation in Qatar. 356 adults were included for analysis. Predictors for progression of COVID-19 were all measured at disease onset and first contact with the health system. Results: The C statistic was 83% (95% CI: 78%-87%) and the calibration plot showed that the model was well-calibrated. Conclusion: The published prognostic model for the progression of COVID-19 infection showed satisfactory discrimination and calibration and the model is easy to apply in clinical practice.d.
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Affiliation(s)
- Shahd A Mohammedain
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
| | - Saif Badran
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
- Department of Plastic Surgery, Hamad Medical Corporation, Doha, Qatar
| | - AbdelNaser Y Elzouki
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - Halla Salim
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - Ayesha Chalaby
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - MYA Siddiqui
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - Yehia Y Hussein
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
| | - Hanan Abdul Rahim
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Lukman Thalib
- Department of Biostatistics, Faculty of Medicine, Istanbul Aydin University, Istanbul, Turkey
| | - Mohammed Fasihul Alam
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | | | - Sumaya Al-Maadeed
- Department of Computer Science, College of Engineering, Qatar University, Doha, Qatar
| | - Suhail AR Doi
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
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4
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Li Z, Li H, Ralescu AL, Dillman JR, Parikh NA, He L. A novel collaborative self-supervised learning method for radiomic data. Neuroimage 2023; 277:120229. [PMID: 37321358 PMCID: PMC10440826 DOI: 10.1016/j.neuroimage.2023.120229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 05/19/2023] [Accepted: 06/12/2023] [Indexed: 06/17/2023] Open
Abstract
The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on labeling radiological images, which is a time-consuming, labor-intensive, and expensive process. In this work, we present the first novel collaborative self-supervised learning method to solve the challenge of insufficient labeled radiomic data, whose characteristics are different from text and image data. To achieve this, we present two collaborative pretext tasks that explore the latent pathological or biological relationships between regions of interest and the similarity and dissimilarity of information between subjects. Our method collaboratively learns the robust latent feature representations from radiomic data in a self-supervised manner to reduce human annotation efforts, which benefits the disease diagnosis. We compared our proposed method with other state-of-the-art self-supervised learning methods on a simulation study and two independent datasets. Extensive experimental results demonstrated that our method outperforms other self-supervised learning methods on both classification and regression tasks. With further refinement, our method will have the potential advantage in automatic disease diagnosis with large-scale unlabeled data available.
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Affiliation(s)
- Zhiyuan Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA; Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
| | - Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Anca L Ralescu
- Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
| | - Jonathan R Dillman
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Nehal A Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, U niversity of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
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5
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Larrateguy S, Vinagre J, Londero F, Dabin J, Ricciardi E, Jeanpaul S, Torres-Castro R, Núñez-Cortés R, Sánchez-Ramírez D, Gimeno-Santos E, Blanco I. Clinical Variables Related to Functional Capacity and Exertional Desaturation in Patients with COVID-19. Biomedicines 2023; 11:2051. [PMID: 37509690 PMCID: PMC10377716 DOI: 10.3390/biomedicines11072051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/01/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Impaired functional capacity is one of the most commonly reported consequences among post-COVID-19 patients. This study aimed to analyse the clinical variables related to functional capacity and exertional desaturation in post-COVID-19 patients at the time of hospital discharge. A cross-sectional study was conducted on patients recovering from COVID-19 pneumonia. The main outcomes measures were functional capacity, assessed using the 1 min sit-to-stand test (1 min STST), and exertional desaturation, defined as a drop of ≥4% in the arterial oxygen saturation. Factors used to characterise the participant outcomes included the use of a high-flow nasal cannula (HFNC), prolonged hospitalisation, occurrence of pulmonary embolism during hospitalisation, and underlying comorbidities. A total of 381 participants (mean age = 53.7 ± 13.2 years, 65.6% men) were included. Participants completed a mean of 16.9 ± 6.2 repetitions in the 1 min STST. Exertional desaturation was observed in 51% of the patients. Higher odds of exertional desaturation were found in the participants who used a HFNC (OR = 3.6; 95%CI: 1.6 to 7.8), were admitted in the hospital >10 days (OR = 4.2; 95%CI: 2.6 to 6.8), and had a pulmonary embolism (OR = 3.5; 95%CI: 2.2. to 5.3). Use of a HFNC (β = -3.4; 95%CI: -5.3 to -1.44), a hospital stay >10 days (β = -2.2; 95%CI: -3.4 to -0.9), and a history of pulmonary embolism (β = -1.4; 95%CI: -2.6 to -0.2) were also negatively associated with the 1 min STST. Most post-COVID-19 patients exhibited reduced functional capacity at the time of hospital discharge, and approximately half had exertional desaturation after the 1 min STST. The use of a HFNC, prolonged hospitalisation and pulmonary embolism were the main clinical variables associated with worse a 1 min STST performance and a higher likelihood of exertional desaturation.
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Affiliation(s)
- Santiago Larrateguy
- Servicio de Kinesiología y Fisioterapia, Hospital de la Baxada “Dra. Teresa Ratto”, Paraná 3100, Argentina; (S.L.); (J.V.); (F.L.); (J.D.); (E.R.); (S.J.)
- Facultad de Ciencias de la Salud, Universidad Adventista del Plata, Libertador San Martin 3103, Argentina
| | - Julian Vinagre
- Servicio de Kinesiología y Fisioterapia, Hospital de la Baxada “Dra. Teresa Ratto”, Paraná 3100, Argentina; (S.L.); (J.V.); (F.L.); (J.D.); (E.R.); (S.J.)
| | - Federico Londero
- Servicio de Kinesiología y Fisioterapia, Hospital de la Baxada “Dra. Teresa Ratto”, Paraná 3100, Argentina; (S.L.); (J.V.); (F.L.); (J.D.); (E.R.); (S.J.)
| | - Johana Dabin
- Servicio de Kinesiología y Fisioterapia, Hospital de la Baxada “Dra. Teresa Ratto”, Paraná 3100, Argentina; (S.L.); (J.V.); (F.L.); (J.D.); (E.R.); (S.J.)
| | - Evangelina Ricciardi
- Servicio de Kinesiología y Fisioterapia, Hospital de la Baxada “Dra. Teresa Ratto”, Paraná 3100, Argentina; (S.L.); (J.V.); (F.L.); (J.D.); (E.R.); (S.J.)
| | - Santiago Jeanpaul
- Servicio de Kinesiología y Fisioterapia, Hospital de la Baxada “Dra. Teresa Ratto”, Paraná 3100, Argentina; (S.L.); (J.V.); (F.L.); (J.D.); (E.R.); (S.J.)
| | - Rodrigo Torres-Castro
- Department of Physical Therapy, Faculty of Medicine, University of Chile, Santiago 8380453, Chile; (R.T.-C.); (R.N.-C.)
- Department of Pulmonary Medicine, Hospital Clínic, University of Barcelona, 08036 Barcelona, Spain;
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain
| | - Rodrigo Núñez-Cortés
- Department of Physical Therapy, Faculty of Medicine, University of Chile, Santiago 8380453, Chile; (R.T.-C.); (R.N.-C.)
- Physiotherapy in Motion Multispeciality Research Group (PTinMOTION), Department of Physiotherapy, University of Valencia, 46003 Valencia, Spain
| | - Diana Sánchez-Ramírez
- Department of Respiratory Therapy, College of Rehabilitation Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada;
| | - Elena Gimeno-Santos
- Department of Pulmonary Medicine, Hospital Clínic, University of Barcelona, 08036 Barcelona, Spain;
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain
- Biomedical Research Networking Center on Respiratory Diseases (CIBERES), 30627 Madrid, Spain
- Barcelona Institute for Global Health (ISGlobal), 08036 Barcelona, Spain
| | - Isabel Blanco
- Department of Pulmonary Medicine, Hospital Clínic, University of Barcelona, 08036 Barcelona, Spain;
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain
- Biomedical Research Networking Center on Respiratory Diseases (CIBERES), 30627 Madrid, Spain
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Krishna C, Kumar D, Kushwaha DS. A Comprehensive Survey on Pandemic Patient Monitoring System: Enabling Technologies, Opportunities, and Research Challenges. Wirel Pers Commun 2023; 131:1-48. [PMID: 37360140 PMCID: PMC10235850 DOI: 10.1007/s11277-023-10535-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/21/2023] [Indexed: 06/28/2023]
Abstract
Sporadic occurrences of transmissible diseases have severe and long-lasting effects on humankind throughout history. These outbreaks have molded the political, economic, and social aspects of human life. Pandemics have redefined some of the basic beliefs of modern healthcare, pushing researchers and scientists to develop innovative solutions to be better equipped for future emergencies. Numerous attempts have been made to fight Covid-19-like pandemics using technologies such as the Internet of Things, wireless body area network, blockchain, and machine learning. Since the disease is highly contagious, novel research in patients' health monitoring system is essential for the constant monitoring of pandemic patients with minimal or no human intervention. With the ongoing pandemic of SARS-CoV-2, popularly known as Covid-19, innovations for monitoring of patients' vitals and storing them securely have risen more than ever. Analyzing the stored patients' data can further assist healthcare workers in their decision-making process. In this paper, we surveyed the research works on remote monitoring of pandemic patients admitted in hospitals or quarantined at home. First, an overview of pandemic patient monitoring is given followed by a brief introduction of enabling technologies i.e. Internet of Things, blockchain, and machine learning to implement the system. The reviewed works have been classified into three categories; remote monitoring of pandemic patients using IoT, blockchain-based storage or sharing platforms for patients' data, and processing/analyzing the stored patients' data using machine learning for prognosis and diagnosis. We also identified several open research issues to set directions for future research.
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Affiliation(s)
- Charu Krishna
- Department of Computer Science & Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, UP 211004 India
| | - Dinesh Kumar
- Department of Computer Science & Engineering, National Institute of Technology Jamshedpur, Jamshedpur, Jharkhand 831014 India
| | - Dharmender Singh Kushwaha
- Department of Computer Science & Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, UP 211004 India
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Zhang Z, Zeng T, Wang Y, Su Y, Tian X, Ma G, Luan Z, Li F. Prediction Model of hospitalization time of COVID-19 patients based on Gradient Boosted Regression Trees. Math Biosci Eng 2023; 20:10444-10458. [PMID: 37322941 DOI: 10.3934/mbe.2023459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
When an outbreak of COVID-19 occurs, it will cause a shortage of medical resources and the surge of demand for hospital beds. Predicting the length of stay (LOS) of COVID-19 patients is helpful to the overall coordination of hospital management and improves the utilization rate of medical resources. The purpose of this paper is to predict LOS for patients with COVID-19, so as to provide hospital management with auxiliary decision-making of medical resource scheduling. We collected the data of 166 COVID-19 patients in a hospital in Xinjiang from July 19, 2020, to August 26, 2020, and carried out a retrospective study. The results showed that the median LOS was 17.0 days, and the average of LOS was 18.06 days. Demographic data and clinical indicators were included as predictive variables to construct a model for predicting the LOS using gradient boosted regression trees (GBRT). The MSE, MAE and MAPE of the model are 23.84, 4.12 and 0.76 respectively. The importance of all the variables involved in the prediction of the model was analyzed, and the clinical indexes creatine kinase-MB (CK-MB), C-reactive protein (CRP), creatine kinase (CK), white blood cell count (WBC) and the age of patients had a higher contribution to the LOS. We found our GBRT model can accurately predict the LOS of COVID-19 patients, which will provide good assistant decision-making for medical management.
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Affiliation(s)
- Zhihao Zhang
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China
| | - Ting Zeng
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China
- School of Public Health, Xinjiang Medical University, Urumqi 830017, China
| | - Yijia Wang
- College of Mathematics and System Science, Xinjiang University, Urumqi 830017, China
| | - Yinxia Su
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China
| | - Xianghua Tian
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China
| | - Guoxiang Ma
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China
| | - Zemin Luan
- School of Public Health, Xinjiang Medical University, Urumqi 830017, China
| | - Fengjun Li
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China
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Rodriguez-Obregon DE, Mejia-Rodriguez AR, Cendejas-Zaragoza L, Gutiérrez Mejía J, Arce-Santana ER, Charleston-Villalobos S, Aljama-Corrales T, Gabutti A, Santos-Díaz A. Semi-Supervised COVID-19 Volumetric Pulmonary Lesion Estimation on CT Images using Probabilistic Active Contour and CNN Segmentation. Biomed Signal Process Control 2023; 85:104905. [PMID: 36993838 PMCID: PMC10030333 DOI: 10.1016/j.bspc.2023.104905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 03/11/2023] [Accepted: 03/18/2023] [Indexed: 03/24/2023]
Abstract
Purpose A semi-supervised two-step methodology is proposed to obtain a volumetric estimation of COVID-19-related lesions on Computed Tomography (CT) images. Methods First, damaged tissue was segmented from CT images using a probabilistic active contours approach. Second, lung parenchyma was extracted using a previously trained U-Net. Finally, volumetric estimation of COVID-19 lesions was calculated considering the lung parenchyma masks. Our approach was validated using a publicly available dataset containing 20 CT COVID-19 images previously labeled and manually segmented. Then, it was applied to 295 COVID-19 patients CT scans admitted to an intensive care unit. We compared the lesion estimation between deceased and survived patients for high and low-resolution images. Results A comparable median Dice similarity coefficient of 0.66 for the 20 validation images was achieved. For the 295 images dataset, results show a significant difference in lesion percentages between deceased and survived patients, with a p-value of 9.1×10−4 in low-resolution and 5.1×10−5 for high-resolution images. Furthermore, the difference in lesion percentages between high and low-resolution images was 10% on average. Conclusion The proposed approach could help estimate the lesion size caused by COVID-19 in CT images and may be considered as an alternative to getting a volumetric segmentation for this novel disease without the requirement of large amounts of COVID-19 labeled data to train an artificial intelligence algorithm. The low variation between the estimated percentage of lesions in high and low-resolution CT images suggests that the proposed approach is robust and It may provide valuable information to differentiate between survived and deceased patients.
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Affiliation(s)
| | | | - Leopoldo Cendejas-Zaragoza
- Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City, Mexico
- Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Juan Gutiérrez Mejía
- Tecnologico de Monterrey, School of Medicine and Health Sciences, Mexico City, Mexico
| | | | | | | | - Alejandro Gabutti
- Department of Radiology and Imaging, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Alejandro Santos-Díaz
- Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City, Mexico
- Tecnologico de Monterrey, School of Medicine and Health Sciences, Monterrey, Mexico
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9
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Elmahdy M, Sebro R. Radiomics analysis in medical imaging research. J Med Radiat Sci 2023; 70:3-7. [PMID: 36762402 PMCID: PMC9977659 DOI: 10.1002/jmrs.662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 01/21/2023] [Indexed: 02/11/2023] Open
Abstract
This article discusses the current research in the field of radiomics in medical imaging with emphasis on its role in fighting coronavirus disease 2019 (COVID-19). This article covers the building of radiomic models in a simple straightforward manner, while discussing radiomic models potential to help us face this pandemic.
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Affiliation(s)
- Mahmoud Elmahdy
- Department of RadiologyMayo ClinicJacksonvilleFloridaUSA,Center for Augmented IntelligenceMayo ClinicJacksonvilleFloridaUSA
| | - Ronnie Sebro
- Department of RadiologyMayo ClinicJacksonvilleFloridaUSA,Center for Augmented IntelligenceMayo ClinicJacksonvilleFloridaUSA,Department of Orthopedic SurgeryMayo ClinicJacksonvilleFloridaUSA,Department of BiostatisticsCentre for Quantitative Health SciencesJacksonvilleFloridaUSA
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10
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Amiri P, Montazeri M, Ghasemian F, Asadi F, Niksaz S, Sarafzadeh F, Khajouei R. Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms. Digit Health 2023; 9:20552076231170493. [PMID: 37312960 PMCID: PMC10259141 DOI: 10.1177/20552076231170493] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 03/31/2023] [Indexed: 06/15/2023] Open
Abstract
Background The severity of coronavirus (COVID-19) in patients with chronic comorbidities is much higher than in other patients, which can lead to their death. Machine learning (ML) algorithms as a potential solution for rapid and early clinical evaluation of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. Objective The objective of this study was to predict the mortality risk and length of stay (LoS) of patients with COVID-19 and history of chronic comorbidities using ML algorithms. Methods This retrospective study was conducted by reviewing the medical records of COVID-19 patients with a history of chronic comorbidities from March 2020 to January 2021 in Afzalipour Hospital in Kerman, Iran. The outcome of patients, hospitalization was recorded as discharge or death. The filtering technique used to score the features and well-known ML algorithms were applied to predict the risk of mortality and LoS of patients. Ensemble Learning methods is also used. To evaluate the performance of the models, different measures including F1, precision, recall, and accuracy were calculated. The TRIPOD guideline assessed transparent reporting. Results This study was performed on 1291 patients, including 900 alive and 391 dead patients. Shortness of breath (53.6%), fever (30.1%), and cough (25.3%) were the three most common symptoms in patients. Diabetes mellitus(DM) (31.3%), hypertension (HTN) (27.3%), and ischemic heart disease (IHD) (14.2%) were the three most common chronic comorbidities of patients. Twenty-six important factors were extracted from each patient's record. Gradient boosting model with 84.15% accuracy was the best model for predicting mortality risk and multilayer perceptron (MLP) with rectified linear unit function (MSE = 38.96) was the best model for predicting the LoS. The most common chronic comorbidities among these patients were DM (31.3%), HTN (27.3%), and IHD (14.2%). The most important factors in predicting the risk of mortality were hyperlipidemia, diabetes, asthma, and cancer, and in predicting LoS was shortness of breath. Conclusion The results of this study showed that the use of ML algorithms can be a good tool to predict the risk of mortality and LoS of patients with COVID-19 and chronic comorbidities based on physiological conditions, symptoms, and demographic information of patients. The Gradient boosting and MLP algorithms can quickly identify patients at risk of death or long-term hospitalization and notify physicians to do appropriate interventions.
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Affiliation(s)
- Parastoo Amiri
- Student Research Committee, Kerman University of Medical Sciences, Kerman, Iran
| | - Mahdieh Montazeri
- Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran
| | - Fahimeh Ghasemian
- Computer Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Fatemeh Asadi
- Student Research Committee, School of Management and Medical Information, Kerman University of Medical Sciences, Kerman, Iran
| | - Saeed Niksaz
- Computer Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Farhad Sarafzadeh
- Infectious and Internal Medicine Department, Afzalipour Hospital, Kerman University of Medical Sciences, Kerman, Iran
| | - Reza Khajouei
- Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran
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11
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Rajamani KT, Rani P, Siebert H, ElagiriRamalingam R, Heinrich MP. Attention-augmented U-Net (AA-U-Net) for semantic segmentation. Signal Image Video Process 2023; 17:981-989. [PMID: 35910403 PMCID: PMC9311338 DOI: 10.1007/s11760-022-02302-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 06/24/2022] [Accepted: 06/27/2022] [Indexed: 05/22/2023]
Abstract
UNLABELLED Deep learning-based image segmentation models rely strongly on capturing sufficient spatial context without requiring complex models that are hard to train with limited labeled data. For COVID-19 infection segmentation on CT images, training data are currently scarce. Attention models, in particular the most recent self-attention methods, have shown to help gather contextual information within deep networks and benefit semantic segmentation tasks. The recent attention-augmented convolution model aims to capture long range interactions by concatenating self-attention and convolution feature maps. This work proposes a novel attention-augmented convolution U-Net (AA-U-Net) that enables a more accurate spatial aggregation of contextual information by integrating attention-augmented convolution in the bottleneck of an encoder-decoder segmentation architecture. A deep segmentation network (U-Net) with this attention mechanism significantly improves the performance of semantic segmentation tasks on challenging COVID-19 lesion segmentation. The validation experiments show that the performance gain of the attention-augmented U-Net comes from their ability to capture dynamic and precise (wider) attention context. The AA-U-Net achieves Dice scores of 72.3% and 61.4% for ground-glass opacity and consolidation lesions for COVID-19 segmentation and improves the accuracy by 4.2% points against a baseline U-Net and 3.09% points compared to a baseline U-Net with matched parameters. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s11760-022-02302-3.
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Affiliation(s)
| | - Priya Rani
- Applied Artificial Intelligence Institute, Deakin University, Burwood, VIC 3125 Australia
| | - Hanna Siebert
- Institute of Medical Informatics, University of Lübeck, Luebeck, Germany
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12
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Arif YA, Stefanko AM, Garcia N, Beshai DA, Fan W, Wong ND. Estimated Atherosclerotic Cardiovascular Disease Risk: Disparities and Severe COVID-19 Outcomes (from the National COVID Cohort Collaborative). Am J Cardiol 2022; 183:16-23. [PMID: 36175254 PMCID: PMC9513339 DOI: 10.1016/j.amjcard.2022.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/27/2022] [Accepted: 08/06/2022] [Indexed: 11/25/2022]
Abstract
Although cardiovascular disease risk factors relate to COVID-19, the association of estimated atherosclerotic cardiovascular disease (ASCVD) risk with severe COVID-19 is not established. We examined the relation of the pooled-cohort ASCVD risk score to severe COVID-19 among 28,646 subjects from the National COVID Cohort Collaborative database who had positive SARS-CoV-2 test results from April 1, 2020 to April 1, 2021. In addition, 10-year ASCVD risk scores were calculated, and subjects were stratified into low-risk (<5%), borderline-risk (5% to <7.5%), intermediate-risk (7.5% to <20%), and high-risk (>=20%) groups. Severe COVID-19 outcomes (including death, remdesivir treatment, COVID-19 pneumonia, acute respiratory distress syndrome, and mechanical ventilation) occurring during follow-up were examined individually and as a composite in relation to ASCVD risk group across race and gender. Multiple logistic regression, adjusted for age, gender, and race, examined the relation of ASCVD risk group to the odds of severe COVID-19 outcomes. Our subjects had a mean age of 59.4 years; 14% were black and 57% were female. ASCVD risk group was directly related to severe COVID-19 prevalence. The adjusted odds ratio of the severe composite COVID-19 outcome by risk group (vs the low-risk group) was 1.8 (95% confidence interval 1.5 to 2.2) for the borderline-risk, 2.7 (2.3 to 3.2) for the intermediate-risk, and 4.6 (3.7 to 5.6) for the high-risk group. Black men and black women in the high-risk group showed higher severe COVID-19 prevalence compared with nonblack men and nonblack women. Prevalence of severe COVID-19 outcomes was similar in intermediate-risk black men and high-risk nonblack men (approximately 12%). In conclusion, although further research is needed, the 10-year ASCVD risk score in adults ages 40 to 79 years may be used to identify those who are at highest risk for COVID-19 complications and for whom more intensive treatment may be warranted.
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Affiliation(s)
- Yousif A Arif
- Heart Disease Prevention Program, Division of Cardiology, University of California, Irvine, Irvine, California
| | - Alexa M Stefanko
- Heart Disease Prevention Program, Division of Cardiology, University of California, Irvine, Irvine, California
| | - Nicholas Garcia
- Heart Disease Prevention Program, Division of Cardiology, University of California, Irvine, Irvine, California
| | - David A Beshai
- Heart Disease Prevention Program, Division of Cardiology, University of California, Irvine, Irvine, California
| | - Wenjun Fan
- Heart Disease Prevention Program, Division of Cardiology, University of California, Irvine, Irvine, California
| | - Nathan D Wong
- Heart Disease Prevention Program, Division of Cardiology, University of California, Irvine, Irvine, California.
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13
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Jalil Z, Abbasi A, Javed AR, Khan MB, Abul Hasanat MH, Altameem A, Alkhathami M, Jilani Saudagar AK, Lydia L. A Novel Benchmark Dataset for COVID-19 Detection during Third Wave in Pakistan. Computational Intelligence and Neuroscience 2022; 2022:1-12. [PMID: 35990145 PMCID: PMC9391128 DOI: 10.1155/2022/6354579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 07/04/2022] [Accepted: 07/13/2022] [Indexed: 11/17/2022]
Abstract
Coronavirus (COVID-19) is a highly severe infection caused by the severe acute respiratory coronavirus 2 (SARS-CoV-2). The polymerase chain reaction (PCR) test is essential to confirm the COVID-19 infection, but it has certain limitations, including paucity of reagents, is computationally time-consuming, and requires expert clinicians. Clinicians suggest that the PCR test is not a reliable automated COVID-19 patient detection system. This study proposed a machine learning-based approach to evaluate the PCR role in COVID-19 detection. We collect real data containing 603 COVID-19 samples from the Pakistan Institute of Medical Sciences (PIMS) Hospital in Islamabad, Pakistan, during the third COVID-19 wave. The experiments are separated into two sets. The first set comprises 24 features, including PCR test results, whereas the second comprises 24 features without PCR test. The findings demonstrate that the decision tree achieves the best detection rate for positive and negative COVID-19 patients in both scenarios. The findings reveal that PCR does not contribute to detecting COVID-19 patients. The findings also aid in the early detection of COVID-19, mainly when PCR test results are insufficient for diagnosing COVID-19 and help developing countries with a paucity of PCR tests and specialist facilities.
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14
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Sharma S, Gupta S, Gupta D, Rashid J, Juneja S, Kim J, Elarabawy MM. Performance Evaluation of the Deep Learning Based Convolutional Neural Network Approach for the Recognition of Chest X-Ray Images. Front Oncol 2022; 12:932496. [PMID: 35847931 PMCID: PMC9277772 DOI: 10.3389/fonc.2022.932496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 06/01/2022] [Indexed: 11/30/2022] Open
Abstract
Recent advancement in the field of deep learning has provided promising performance for the analysis of medical images. Every year, pneumonia is the leading cause for death of various children under the age of 5 years. Chest X-rays are the first technique that is used for the detection of pneumonia. Various deep learning and computer vision techniques can be used to determine the virus which causes pneumonia using Chest X-ray images. These days, it is possible to use Convolutional Neural Networks (CNN) for the classification and analysis of images due to the availability of a large number of datasets. In this work, a CNN model is implemented for the recognition of Chest X-ray images for the detection of Pneumonia. The model is trained on a publicly available Chest X-ray images dataset having two classes: Normal chest X-ray images and Pneumonic Chest X-ray images, where each class has 5000 Samples. 80% of the collected data is used for the purpose to train the model, and the rest for testing the model. The model is trained and validated using two optimizers: Adam and RMSprop. The maximum recognition accuracy of 98% is obtained on the validation dataset. The obtained results are further compared with the results obtained by other researchers for the recognition of biomedical images.
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Affiliation(s)
- Sandhya Sharma
- Chitkara University Institute of Engineering and Technology, Chitkara University, Baddi, India
| | - Sheifali Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, India
| | - Deepali Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, India
| | - Junaid Rashid
- Department of Computer Science and Engineering, Kongju National University, Cheonan, South Korea
| | | | - Jungeun Kim
- Department of Computer Science and Engineering, Kongju National University, Cheonan, South Korea.,Department of Software, Kongju National University, Cheonan, South Korea
| | - Mahmoud M Elarabawy
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia.,Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia, Egypt
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15
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Mori M, Alborghetti L, Palumbo D, Broggi S, Raspanti D, Rovere Querini P, Del Vecchio A, De Cobelli F, Fiorino C. Atlas-Based Lung Segmentation Combined With Automatic Densitometry Characterization In COVID-19 Patients: Training, Validation And First Application In A Longitudinal Study. Phys Med 2022; 100:142-152. [PMID: 35839667 PMCID: PMC9250926 DOI: 10.1016/j.ejmp.2022.06.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 06/15/2022] [Accepted: 06/29/2022] [Indexed: 11/16/2022] Open
Abstract
Purpose To develop and validate an automated segmentation tool for COVID-19 lung CTs. To combine it with densitometry information in identifying Aerated, Intermediate and Consolidated Volumes in admission (CT1) and follow up CT (CT3). Materials and Methods An Atlas was trained on manually segmented CT1 of 250 patients and validated on 10 CT1 of the training group, 10 new CT1 and 10 CT3, by comparing DICE index between automatic (AUTO), automatic-corrected (AUTOMAN) and manual (MAN) contours. A previously developed automatic method was applied on HU lung density histograms to quantify Aerated, Intermediate and Consolidated Volumes. Volumes of subregions in validation CT1 and CT3 were quantified for each method. Results In validation CT1/CT3, manual correction of automatic contours was not necessary in 40% of cases. Mean DICE values for both lungs were 0.94 for AUTOVsMAN and 0.96 for AUTOMANVsMAN. Differences between Aerated and Intermediate Volumes quantified with AUTOVsMAN contours were always < 6%. Consolidated Volumes showed larger differences (mean: −95 ± 72 cc). If considering AUTOMANVsMAN volumes, differences got further smaller for Aerated and Intermediate, and were drastically reduced for consolidated Volumes (mean: −36 ± 25 cc). The average time for manual correction of automatic lungs contours on CT1 was 5 ± 2 min. Conclusions An Atlas for automatic segmentation of lungs in COVID-19 patients was developed and validated. Combined with a previously developed method for lung densitometry characterization, it provides a fast, operator-independent way to extract relevant quantitative parameters with minimal manual intervention.
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Affiliation(s)
- Martina Mori
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy.
| | - Lisa Alborghetti
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - Diego Palumbo
- Radiology, San Raffaele Scientific Institute, Milano, Italy
| | - Sara Broggi
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | | | - Patrizia Rovere Querini
- Internal Medecine, San Raffaele Scientific Institute, Milano, Italy; Faculty of Medicine and Surgery, Vita-Salute San Raffaele University, Milano, Italy
| | | | - Francesco De Cobelli
- Radiology, San Raffaele Scientific Institute, Milano, Italy; Faculty of Medicine and Surgery, Vita-Salute San Raffaele University, Milano, Italy
| | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
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16
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Prosepe I, Groenwold RHH, Knevel R, Pajouheshnia R, van Geloven N. The Disconnect Between Development and Intended Use of Clinical Prediction Models for Covid-19: A Systematic Review and Real-World Data Illustration. Front Epidemiol 2022; 2:899589. [PMID: 38455309 PMCID: PMC10910889 DOI: 10.3389/fepid.2022.899589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 05/23/2022] [Indexed: 03/09/2024]
Abstract
Background The SARS-CoV-2 pandemic has boosted the appearance of clinical predictions models in medical literature. Many of these models aim to provide guidance for decision making on treatment initiation. Special consideration on how to account for post-baseline treatments is needed when developing such models. We examined how post-baseline treatment was handled in published Covid-19 clinical prediction models and we illustrated how much estimated risks may differ according to how treatment is handled. Methods Firstly, we reviewed 33 Covid-19 prognostic models published in literature in the period up to 5 May 2020. We extracted: (1) the reported intended use of the model; (2) how treatment was incorporated during model development and (3) whether the chosen analysis strategy was in agreement with the intended use. Secondly, we used nationwide Dutch data on hospitalized patients who tested positive for SARS-CoV-2 in 2020 to illustrate how estimated mortality risks will differ when using four different analysis strategies to model ICU treatment. Results Of the 33 papers, 21 (64%) had misalignment between intended use and analysis strategy, 7 (21%) were unclear about the estimated risk and only 5 (15%) had clear alignment between intended use and analysis strategy. We showed with real data how different approaches to post-baseline treatment yield different estimated mortality risks, ranging between 33 and 46% for a 75 year-old patient with two medical conditions. Conclusions Misalignment between intended use and analysis strategy is common in reported Covid-19 clinical prediction models. This can lead to considerable under or overestimation of intended risks.
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Affiliation(s)
- Ilaria Prosepe
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
| | - Rolf H. H. Groenwold
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, Netherlands
| | - Rachel Knevel
- Department of Rheumatology, Leiden University Medical Center, Leiden, Netherlands
| | - Romin Pajouheshnia
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, Netherlands
| | - Nan van Geloven
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
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17
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Bermejo-Peláez D, San José Estépar R, Fernández-Velilla M, Palacios Miras C, Gallardo Madueño G, Benegas M, Gotera Rivera C, Cuerpo S, Luengo-Oroz M, Sellarés J, Sánchez M, Bastarrika G, Peces Barba G, Seijo LM, Ledesma-Carbayo MJ. Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT. Sci Rep 2022; 12:9387. [PMID: 35672437 PMCID: PMC9172615 DOI: 10.1038/s41598-022-13298-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 05/12/2022] [Indexed: 12/15/2022] Open
Abstract
The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score.
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Affiliation(s)
- David Bermejo-Peláez
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Av Complutense 30, 28040, Madrid, Spain
- CIBER-BBN, Madrid, Spain
- , Spotlab, Madrid, Spain
| | | | | | | | | | | | | | - Sandra Cuerpo
- Hospital Clinic de Barcelona-IDIBPAS, Barcelona, Spain
- CIBER-ES, Madrid, Spain
| | | | - Jacobo Sellarés
- Hospital Clinic de Barcelona-IDIBPAS, Barcelona, Spain
- CIBER-ES, Madrid, Spain
- Universidad de Vic (UVIC), Vic, Spain
| | | | | | - German Peces Barba
- Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain
- CIBER-ES, Madrid, Spain
| | - Luis M Seijo
- Clínica Universidad de Navarra, Pamplona, Spain
- CIBER-ES, Madrid, Spain
| | - María J Ledesma-Carbayo
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Av Complutense 30, 28040, Madrid, Spain.
- CIBER-BBN, Madrid, Spain.
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18
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Shiri I, Salimi Y, Pakbin M, Hajianfar G, Avval AH, Sanaat A, Mostafaei S, Akhavanallaf A, Saberi A, Mansouri Z, Askari D, Ghasemian M, Sharifipour E, Sandoughdaran S, Sohrabi A, Sadati E, Livani S, Iranpour P, Kolahi S, Khateri M, Bijari S, Atashzar MR, Shayesteh SP, Khosravi B, Babaei MR, Jenabi E, Hasanian M, Shahhamzeh A, Foroghi Ghomi SY, Mozafari A, Teimouri A, Movaseghi F, Ahmari A, Goharpey N, Bozorgmehr R, Shirzad-Aski H, Mortazavi R, Karimi J, Mortazavi N, Besharat S, Afsharpad M, Abdollahi H, Geramifar P, Radmard AR, Arabi H, Rezaei-Kalantari K, Oveisi M, Rahmim A, Zaidi H. COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients. Comput Biol Med 2022; 145:105467. [PMID: 35378436 PMCID: PMC8964015 DOI: 10.1016/j.compbiomed.2022.105467] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 03/24/2022] [Accepted: 03/26/2022] [Indexed: 12/16/2022]
Abstract
BACKGROUND We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. METHODS Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. RESULTS In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. CONCLUSION Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Masoumeh Pakbin
- Imaging Department, Qom University of Medical Sciences, Qum, Iran
| | - Ghasem Hajianfar
- Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran
| | | | - Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Shayan Mostafaei
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Abdollah Saberi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Dariush Askari
- Department of Radiology Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammadreza Ghasemian
- Department of Radiology, Shahid Beheshti Hospital, Qom University of Medical Sciences, Qum, Iran
| | - Ehsan Sharifipour
- Neuroscience Research Center, Qom University of Medical Sciences, Qum, Iran
| | - Saleh Sandoughdaran
- Men's Health and Reproductive Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Sohrabi
- Cancer Control Research Center, Cancer Control Foundation, Iran University of Medical Sciences, Tehran, Iran
| | - Elham Sadati
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Somayeh Livani
- Clinical Research Development Unit (CRDU), Sayad Shirazi Hospital, Golestan University of Medical Sciences, Gorgan, Iran
| | - Pooya Iranpour
- Medical Imaging Research Center, Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shahriar Kolahi
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Maziar Khateri
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Tehran, Iran
| | - Salar Bijari
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mohammad Reza Atashzar
- Department of Immunology, School of Medicine, Fasa University of Medical Sciences, Fasa, Iran
| | - Sajad P. Shayesteh
- Department of Physiology, Pharmacology and Medical Physics, Alborz University of Medical Sciences, Karaj, Iran
| | - Bardia Khosravi
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Babaei
- Department of Interventional Radiology, Firouzgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Elnaz Jenabi
- Research Centre for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Hasanian
- Department of Radiology, Arak University of Medical Sciences, Arak, Iran
| | - Alireza Shahhamzeh
- Clinical Research Development Center, Qom University of Medical Sciences, Qum, Iran
| | - Seyaed Yaser Foroghi Ghomi
- Clinical Research Development Center, Shahid Beheshti Hospital, Qom University Of Medical Sciences, Qom, Iran
| | - Abolfazl Mozafari
- Department of Medical Sciences, Qom Branch, Islamic Azad University, Qum, Iran
| | - Arash Teimouri
- Medical Imaging Research Center, Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Fatemeh Movaseghi
- Department of Medical Sciences, Qom Branch, Islamic Azad University, Qum, Iran
| | - Azin Ahmari
- Ayatolah Khansary Hospital, Arak University of Medical Sciences, Arak, Iran
| | - Neda Goharpey
- Department of Radiation Oncology, Shohadaye Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Rama Bozorgmehr
- Clinical Research Development Unit, Shohadaye Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Roozbeh Mortazavi
- Department of Internal Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Jalal Karimi
- Department of Infectious Disease, School of Medicine, Fasa University of Medical Sciences, Fasa, Iran
| | - Nazanin Mortazavi
- Dental Research Center, Golestan University of Medical Sciences, Gorgan, Iran
| | - Sima Besharat
- Golestan Research Center of Gastroenterology and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran
| | - Mandana Afsharpad
- Cancer Control Research Center, Cancer Control Foundation, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Technology, Faculty of Allied Medical Sciences, Kerman University of Medical Sciences, Kerman, Iran
| | - Parham Geramifar
- Research Centre for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Reza Radmard
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Kiara Rezaei-Kalantari
- Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran
| | - Mehrdad Oveisi
- Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, King’s College London, London, United Kingdom
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada,Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland,Geneva University Neurocenter, Geneva University, Geneva, Switzerland,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark,Corresponding author. Geneva University Hospital Division of Nuclear Medicine and Molecular Imaging, CH-1211, Geneva, Switzerland
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19
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Meraihi Y, Gabis AB, Mirjalili S, Ramdane-Cherif A, Alsaadi FE. Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey. SN Comput Sci 2022; 3:286. [PMID: 35578678 PMCID: PMC9096341 DOI: 10.1007/s42979-022-01184-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 04/30/2022] [Indexed: 12/12/2022]
Abstract
The year 2020 experienced an unprecedented pandemic called COVID-19, which impacted the whole world. The absence of treatment has motivated research in all fields to deal with it. In Computer Science, contributions mainly include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Data science and Machine Learning (ML) are the most widely used techniques in this area. This paper presents an overview of more than 160 ML-based approaches developed to combat COVID-19. They come from various sources like Elsevier, Springer, ArXiv, MedRxiv, and IEEE Xplore. They are analyzed and classified into two categories: Supervised Learning-based approaches and Deep Learning-based ones. In each category, the employed ML algorithm is specified and a number of used parameters is given. The parameters set for each of the algorithms are gathered in different tables. They include the type of the addressed problem (detection, diagnosis, or detection), the type of the analyzed data (Text data, X-ray images, CT images, Time series, Clinical data,...) and the evaluated metrics (accuracy, precision, sensitivity, specificity, F1-Score, and AUC). The study discusses the collected information and provides a number of statistics drawing a picture about the state of the art. Results show that Deep Learning is used in 79% of cases where 65% of them are based on the Convolutional Neural Network (CNN) and 17% use Specialized CNN. On his side, supervised learning is found in only 16% of the reviewed approaches and only Random Forest, Support Vector Machine (SVM) and Regression algorithms are employed.
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Affiliation(s)
- Yassine Meraihi
- LIST Laboratory, University of M'Hamed Bougara Boumerdes, Avenue of Independence, 35000 Boumerdes, Algeria
| | - Asma Benmessaoud Gabis
- Ecole nationale Supérieure d'Informatique, Laboratoire des Méthodes de Conception des Systèmes, BP 68 M, 16309 Oued-Smar, Alger Algeria
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006 Australia.,Yonsei Frontier Lab, Yonsei University, Seoul, Korea
| | - Amar Ramdane-Cherif
- LISV Laboratory, University of Versailles St-Quentin-en-Yvelines, 10-12 Avenue of Europe, 78140 Velizy, France
| | - Fawaz E Alsaadi
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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20
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Li Y, Kong Y, Ebell MH, Martinez L, Cai X, Lennon RP, Tarn DM, Mainous AG, Zgierska AE, Barrett B, Tuan WJ, Maloy K, Goyal M, Krist AH, Gal TS, Sung MH, Li C, Jin Y, Shen Y. Development and Validation of a Two-Step Predictive Risk Stratification Model for Coronavirus Disease 2019 In-hospital Mortality: A Multicenter Retrospective Cohort Study. Front Med (Lausanne) 2022; 9:827261. [PMID: 35463024 PMCID: PMC9021426 DOI: 10.3389/fmed.2022.827261] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 01/24/2022] [Indexed: 12/24/2022] Open
Abstract
Objectives An accurate prognostic score to predict mortality for adults with COVID-19 infection is needed to understand who would benefit most from hospitalizations and more intensive support and care. We aimed to develop and validate a two-step score system for patient triage, and to identify patients at a relatively low level of mortality risk using easy-to-collect individual information. Design Multicenter retrospective observational cohort study. Setting Four health centers from Virginia Commonwealth University, Georgetown University, the University of Florida, and the University of California, Los Angeles. Patients Coronavirus Disease 2019-confirmed and hospitalized adult patients. Measurements and Main Results We included 1,673 participants from Virginia Commonwealth University (VCU) as the derivation cohort. Risk factors for in-hospital death were identified using a multivariable logistic model with variable selection procedures after repeated missing data imputation. A two-step risk score was developed to identify patients at lower, moderate, and higher mortality risk. The first step selected increasing age, more than one pre-existing comorbidities, heart rate >100 beats/min, respiratory rate ≥30 breaths/min, and SpO2 <93% into the predictive model. Besides age and SpO2, the second step used blood urea nitrogen, absolute neutrophil count, C-reactive protein, platelet count, and neutrophil-to-lymphocyte ratio as predictors. C-statistics reflected very good discrimination with internal validation at VCU (0.83, 95% CI 0.79-0.88) and external validation at the other three health systems (range, 0.79-0.85). A one-step model was also derived for comparison. Overall, the two-step risk score had better performance than the one-step score. Conclusions The two-step scoring system used widely available, point-of-care data for triage of COVID-19 patients and is a potentially time- and cost-saving tool in practice.
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Affiliation(s)
- Yang Li
- Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China.,RSS and China-Re Life Joint Lab on Public Health and Risk Management, Renmin University of China, Beijing, China
| | - Yanlei Kong
- Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China
| | - Mark H Ebell
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, United States
| | - Leonardo Martinez
- Department of Epidemiology, School of Public Health, Boston University, Boston, MA, United States
| | - Xinyan Cai
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, United States
| | - Robert P Lennon
- Department of Family and Community Medicine, Penn State College of Medicine, Hershey, PA, United States
| | - Derjung M Tarn
- Department of Family Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, United States
| | - Arch G Mainous
- Department of Health Services Research, Management and Policy, University of Florida, Gainesville, FL, United States
| | - Aleksandra E Zgierska
- Departments of Family and Community Medicine, Public Health Sciences, and Anesthesiology and Perioperative Medicine, Penn State College of Medicine, Hershey, PA, United States
| | - Bruce Barrett
- Department of Family Medicine and Community Health, University of Wisconsin, Madison, WI, United States
| | - Wen-Jan Tuan
- Department of Family and Community Medicine, Penn State College of Medicine, Hershey, PA, United States
| | - Kevin Maloy
- Department of Emergency Medicine, MedStar Washington Hospital Center, Washington, DC, United States
| | - Munish Goyal
- Department of Emergency Medicine, MedStar Washington Hospital Center, Washington, DC, United States
| | - Alex H Krist
- Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, VA, United States
| | - Tamas S Gal
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, United States
| | - Meng-Hsuan Sung
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, United States
| | - Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States
| | - Yier Jin
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States
| | - Ye Shen
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, United States
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21
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Vaidya P, Alilou M, Hiremath A, Gupta A, Bera K, Furin J, Armitage K, Gilkeson R, Yuan L, Fu P, Lu C, Ji M, Madabhushi A. An End-to-End Integrated Clinical and CT-Based Radiomics Nomogram for Predicting Disease Severity and Need for Ventilator Support in COVID-19 Patients: A Large Multisite Retrospective Study. Front Radiol 2022; 2:781536. [PMID: 36437821 PMCID: PMC9696643 DOI: 10.3389/fradi.2022.781536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
OBJECTIVE The disease COVID-19 has caused a widespread global pandemic with ~3. 93 million deaths worldwide. In this work, we present three models-radiomics (MRM), clinical (MCM), and combined clinical-radiomics (MRCM) nomogram to predict COVID-19-positive patients who will end up needing invasive mechanical ventilation from the baseline CT scans. METHODS We performed a retrospective multicohort study of individuals with COVID-19-positive findings for a total of 897 patients from two different institutions (Renmin Hospital of Wuhan University, D1 = 787, and University Hospitals, US D2 = 110). The patients from institution-1 were divided into 60% training, D 1 T ( N = 473 ) , and 40% test set D 1 V ( N = 314 ) . The patients from institution-2 were used for an independent validation test set D 2 V ( N = 110 ) . A U-Net-based neural network (CNN) was trained to automatically segment out the COVID consolidation regions on the CT scans. The segmented regions from the CT scans were used for extracting first- and higher-order radiomic textural features. The top radiomic and clinical features were selected using the least absolute shrinkage and selection operator (LASSO) with an optimal binomial regression model within D 1 T . RESULTS The three out of the top five features identified using D 1 T were higher-order textural features (GLCM, GLRLM, GLSZM), whereas the last two features included the total absolute infection size on the CT scan and the total intensity of the COVID consolidations. The radiomics model (MRM) was constructed using the radiomic score built using the coefficients obtained from the LASSO logistic model used within the linear regression (LR) classifier. The MRM yielded an area under the receiver operating characteristic curve (AUC) of 0.754 (0.709-0.799) on D 1 T , 0.836 on D 1 V , and 0.748 D 2 V . The top prognostic clinical factors identified in the analysis were dehydrogenase (LDH), age, and albumin (ALB). The clinical model had an AUC of 0.784 (0.743-0.825) on D 1 T , 0.813 on D 1 V , and 0.688 on D 2 V . Finally, the combined model, MRCM integrating radiomic score, age, LDH and ALB, yielded an AUC of 0.814 (0.774-0.853) on D 1 T , 0.847 on D 1 V , and 0.771 on D 2 V . The MRCM had an overall improvement in the performance of ~5.85% ( D 1 T : p = 0.0031; D 1 V p = 0.0165; D 2 V : p = 0.0369) over MCM. CONCLUSION The novel integrated imaging and clinical model (MRCM) outperformed both models (MRM) and (MCM). Our results across multiple sites suggest that the integrated nomogram could help identify COVID-19 patients with more severe disease phenotype and potentially require mechanical ventilation.
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Affiliation(s)
- Pranjal Vaidya
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Mehdi Alilou
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Amogh Hiremath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY, United States
| | - Jennifer Furin
- Department of Infectious Diseases, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Keith Armitage
- Department of Infectious Diseases, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Robert Gilkeson
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Lei Yuan
- Department of Information Center, Renmin Hospital of Wuhan University, Wuhan, China
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Cheng Lu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Mengyao Ji
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, United States
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22
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Alrajhi AA, Alswailem OA, Wali G, Alnafee K, AlGhamdi S, Alarifi J, AlMuhaideb S, ElMoaqet H, AbuSalah A. Data-Driven Prediction for COVID-19 Severity in Hospitalized Patients. Int J Environ Res Public Health 2022; 19:ijerph19052958. [PMID: 35270653 PMCID: PMC8910504 DOI: 10.3390/ijerph19052958] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 02/24/2022] [Accepted: 02/25/2022] [Indexed: 02/01/2023]
Abstract
Clinicians urgently need reliable and stable tools to predict the severity of COVID-19 infection for hospitalized patients to enhance the utilization of hospital resources and supplies. Published COVID-19 related guidelines are frequently being updated, which impacts its utilization as a stable go-to resource for informing clinical and operational decision-making processes. In addition, many COVID-19 patient-level severity prediction tools that were developed during the early stages of the pandemic failed to perform well in the hospital setting due to many challenges including data availability, model generalization, and clinical validation. This study describes the experience of a large tertiary hospital system network in the Middle East in developing a real-time severity prediction tool that can assist clinicians in matching patients with appropriate levels of needed care for better management of limited health care resources during COVID-19 surges. It also provides a new perspective for predicting patients’ COVID-19 severity levels at the time of hospital admission using comprehensive data collected during the first year of the pandemic in the hospital. Unlike many previous studies for a similar population in the region, this study evaluated 4 machine learning models using a large training data set of 1386 patients collected between March 2020 and April 2021. The study uses comprehensive COVID-19 patient-level clinical data from the hospital electronic medical records (EMR), vital sign monitoring devices, and Polymerase Chain Reaction (PCR) machines. The data were collected, prepared, and leveraged by a panel of clinical and data experts to develop a multi-class data-driven framework to predict severity levels for COVID-19 infections at admission time. Finally, this study provides results from a prospective validation test conducted by clinical experts in the hospital. The proposed prediction framework shows excellent performance in concurrent validation (n=462 patients, March 2020–April 2021) with highest discrimination obtained with the random forest classification model, achieving a macro- and micro-average area under receiver operating characteristics curve (AUC) of 0.83 and 0.87, respectively. The prospective validation conducted by clinical experts (n=185 patients, April–May 2021) showed a promising overall prediction performance with a recall of 78.4–90.0% and a precision of 75.0–97.8% for different severity classes.
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Affiliation(s)
- Abdulrahman A. Alrajhi
- Department of Medicine, King Faisal Specialist Hospital & Research Centre, Riyadh 11211, Saudi Arabia
- Correspondence: (A.A.A.); (O.A.A.); (H.E.)
| | - Osama A. Alswailem
- Healthcare Information & Technology Affairs, King Faisal Specialist Hospital & Research Centre, Riyadh 11211, Saudi Arabia
- Correspondence: (A.A.A.); (O.A.A.); (H.E.)
| | - Ghassan Wali
- Department of Medicine, King Faisal Specialist Hospital & Research Centre, Jeddah 21561, Saudi Arabia;
| | - Khalid Alnafee
- Infection Control & Hospital Epidemiology Department, King Faisal Specialist Hospital & Research Centre, Riyadh 11211, Saudi Arabia;
| | - Sarah AlGhamdi
- Center of Healthcare Intelligence, Health Information & Technology Affairs, King Faisal Specialist Hospital & Research Centre, Riyadh 11211, Saudi Arabia; (S.A.); (J.A.); (A.A.)
| | - Jhan Alarifi
- Center of Healthcare Intelligence, Health Information & Technology Affairs, King Faisal Specialist Hospital & Research Centre, Riyadh 11211, Saudi Arabia; (S.A.); (J.A.); (A.A.)
| | - Sarab AlMuhaideb
- Computer Science Department, College of Computer & Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia;
| | - Hisham ElMoaqet
- Department of Mechatronics Engineering, German Jordanian University, Amman 11180, Jordan
- Correspondence: (A.A.A.); (O.A.A.); (H.E.)
| | - Ahmad AbuSalah
- Center of Healthcare Intelligence, Health Information & Technology Affairs, King Faisal Specialist Hospital & Research Centre, Riyadh 11211, Saudi Arabia; (S.A.); (J.A.); (A.A.)
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23
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Alyasseri ZAA, Al‐Betar MA, Doush IA, Awadallah MA, Abasi AK, Makhadmeh SN, Alomari OA, Abdulkareem KH, Adam A, Damasevicius R, Mohammed MA, Zitar RA. Review on COVID-19 diagnosis models based on machine learning and deep learning approaches. Expert Syst 2022; 39:e12759. [PMID: 34511689 PMCID: PMC8420483 DOI: 10.1111/exsy.12759] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 05/17/2021] [Accepted: 06/07/2021] [Indexed: 05/02/2023]
Abstract
COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.
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Affiliation(s)
- Zaid Abdi Alkareem Alyasseri
- Center for Artificial Intelligence Technology, Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia
- ECE Department‐Faculty of EngineeringUniversity of KufaNajafIraq
| | - Mohammed Azmi Al‐Betar
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Department of Information TechnologyAl‐Huson University College, Al‐Balqa Applied UniversityIrbidJordan
| | - Iyad Abu Doush
- Computing Department, College of Engineering and Applied SciencesAmerican University of KuwaitSalmiyaKuwait
- Computer Science DepartmentYarmouk UniversityIrbidJordan
| | - Mohammed A. Awadallah
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Department of Computer ScienceAl‐Aqsa UniversityGazaPalestine
| | - Ammar Kamal Abasi
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- School of Computer SciencesUniversiti Sains MalaysiaPenangMalaysia
| | - Sharif Naser Makhadmeh
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Faculty of Information TechnologyMiddle East UniversityAmmanJordan
| | | | | | - Afzan Adam
- Center for Artificial Intelligence Technology, Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia
| | | | - Mazin Abed Mohammed
- College of Computer Science and Information TechnologyUniversity of AnbarAnbarIraq
| | - Raed Abu Zitar
- Sorbonne Center of Artificial IntelligenceSorbonne University‐Abu DhabiAbu DhabiUnited Arab Emirates
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Xiong Y, Ma Y, Ruan L, Li D, Lu C, Huang L. Comparing different machine learning techniques for predicting COVID-19 severity. Infect Dis Poverty 2022; 11:19. [PMID: 35177120 PMCID: PMC8851750 DOI: 10.1186/s40249-022-00946-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 02/09/2022] [Indexed: 12/28/2022] Open
Abstract
Background Coronavirus disease 2019 (COVID-19) is still ongoing spreading globally, machine learning techniques were used in disease diagnosis and to predict treatment outcomes, which showed favorable performance. The present study aims to predict COVID-19 severity at admission by different machine learning techniques including random forest (RF), support vector machine (SVM), and logistic regression (LR). Feature importance to COVID-19 severity were further identified. Methods A retrospective design was adopted in the JinYinTan Hospital from January 26 to March 28, 2020, eighty-six demographic, clinical, and laboratory features were selected with LassoCV method, Spearman’s rank correlation, experts’ opinions, and literature evaluation. RF, SVM, and LR were performed to predict severe COVID-19, the performance of the models was compared by the area under curve (AUC). Additionally, feature importance to COVID-19 severity were analyzed by the best performance model. Results A total of 287 patients were enrolled with 36.6% severe cases and 63.4% non-severe cases. The median age was 60.0 years (interquartile range: 49.0–68.0 years). Three models were established using 23 features including 1 clinical, 1 chest computed tomography (CT) and 21 laboratory features. Among three models, RF yielded better overall performance with the highest AUC of 0.970 than SVM of 0.948 and LR of 0.928, RF also achieved a favorable sensitivity of 96.7%, specificity of 69.5%, and accuracy of 84.5%. SVM had sensitivity of 93.9%, specificity of 79.0%, and accuracy of 88.5%. LR also achieved a favorable sensitivity of 92.3%, specificity of 72.3%, and accuracy of 85.2%. Additionally, chest-CT had highest importance to illness severity, and the following features were neutrophil to lymphocyte ratio, lactate dehydrogenase, and D-dimer, respectively. Conclusions Our results indicated that RF could be a useful predictive tool to identify patients with severe COVID-19, which may facilitate effective care and further optimize resources. Graphical Abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s40249-022-00946-4.
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Affiliation(s)
- Yibai Xiong
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16, Nanxiao Street, Dongzhimen, Dongcheng District, Beijing, 100700, Beijing, China
| | - Yan Ma
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16, Nanxiao Street, Dongzhimen, Dongcheng District, Beijing, 100700, Beijing, China
| | - Lianguo Ruan
- Department of Infectious Diseases, JinYinTan Hospital, Wuhan, 430040, China
| | - Dan Li
- Information Center, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Cheng Lu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16, Nanxiao Street, Dongzhimen, Dongcheng District, Beijing, 100700, Beijing, China.
| | - Luqi Huang
- National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, No. 16, Nanxiao Street, Dongzhimen, Dongcheng District, Beijing, 100700, Beijing, China.
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25
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Ortiz A, Trivedi A, Desbiens J, Blazes M, Robinson C, Gupta S, Dodhia R, Bhatraju PK, Liles WC, Lee A, Ferres JML. Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes. Sci Rep 2022; 12:1716. [PMID: 35110593 DOI: 10.1038/s41598-022-05532-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 01/13/2022] [Indexed: 12/23/2022] Open
Abstract
The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from chest computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone. We develop a novel patient-level algorithm to aggregate the chest CT volume into a 2D representation that can be easily integrated with clinical metadata to distinguish COVID-19 pneumonia from chest CT volumes from healthy participants and participants with other viral pneumonia. Furthermore, we present a multitask model for joint segmentation of different classes of pulmonary lesions present in COVID-19 infected lungs that can outperform individual segmentation models for each task. We directly compare this multitask segmentation approach to combining feature-agnostic volumetric CT classification feature maps with clinical metadata for predicting mortality. We show that the combination of features derived from the chest CT volumes improve the AUC performance to 0.80 from the 0.52 obtained by using patients' clinical data alone. These approaches enable the automated extraction of clinically relevant features from chest CT volumes for risk stratification of COVID-19 patients.
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26
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Shah H, Shah S, Tanwar S, Gupta R, Kumar N. Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends. Multimed Syst 2022; 28:1189-1222. [PMID: 34276140 PMCID: PMC8275905 DOI: 10.1007/s00530-021-00818-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 05/29/2021] [Indexed: 05/05/2023]
Abstract
The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought an opportunity to showcase AI and healthcare integration potentials as the current infrastructure worldwide is overwhelmed and crumbling. Due to AI's flexibility and adaptability, it can be used as a tool to tackle COVID-19. Motivated by these facts, in this paper, we surveyed how the AI techniques can handle the COVID-19 pandemic situation and present the merits and demerits of these techniques. This paper presents a comprehensive end-to-end review of all the AI-techniques that can be used to tackle all areas of the pandemic. Further, we systematically discuss the issues of the COVID-19, and based on the literature review, we suggest their potential countermeasures using AI techniques. In the end, we analyze various open research issues and challenges associated with integrating the AI techniques in the COVID-19.
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Affiliation(s)
- Het Shah
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Saiyam Shah
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Sudeep Tanwar
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Rajesh Gupta
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Neeraj Kumar
- Department of Computer Science Engineering, Thapar Institute of Engineering and Technology, Deemed to be University, Patiala, India
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand India
- King Abdul Aziz University, Jeddah, Saudi Arabia
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27
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Soda P, D'Amico NC, Tessadori J, Valbusa G, Guarrasi V, Bortolotto C, Akbar MU, Sicilia R, Cordelli E, Fazzini D, Cellina M, Oliva G, Callea G, Panella S, Cariati M, Cozzi D, Miele V, Stellato E, Carrafiello G, Castorani G, Simeone A, Preda L, Iannello G, Del Bue A, Tedoldi F, Alí M, Sona D, Papa S. AIforCOVID: Predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study. Med Image Anal 2021; 74:102216. [PMID: 34492574 PMCID: PMC8401374 DOI: 10.1016/j.media.2021.102216] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 08/03/2021] [Accepted: 08/18/2021] [Indexed: 01/08/2023]
Abstract
Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether artificial intelligence working with chest X-ray (CXR) scans and clinical data can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. Indeed, further to induce lower radiation dose than computed tomography (CT), CXR is a simpler and faster radiological technique, being also more widespread. In this respect, we present three approaches that use features extracted from CXR images, either handcrafted or automatically learnt by convolutional neuronal networks, which are then integrated with the clinical data. As a further contribution, this work introduces a repository that collects data from 820 patients enrolled in six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, suggesting that clinical data and images have the potential to provide useful information for the management of patients and hospital resources.
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Affiliation(s)
- Paolo Soda
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Via Alvaro del Portillo 21, Rome 00128, Italy.
| | - Natascha Claudia D'Amico
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Via Alvaro del Portillo 21, Rome 00128, Italy; Department of Diagnostic Imaging and Stereotactic Radiosurgey, Centro Diagnostico Italiano S.p.A., Via S. Saint Bon 20, Milan 20147, Italy
| | - Jacopo Tessadori
- Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, Via Morego 30, Genoa 16163, Italy
| | - Giovanni Valbusa
- Bracco Imaging S.p.A., Via Caduti di Marcinelle 13, Milan 20134, Italy
| | - Valerio Guarrasi
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Via Alvaro del Portillo 21, Rome 00128, Italy; Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Via Ariosto, 25, Rome 00185, Italy
| | - Chandra Bortolotto
- Radiology Institute, Fondazione IRCCS Policlinico San Matteo, Viale Golgi 19, Pavia 27100, Italy
| | - Muhammad Usman Akbar
- Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, Via Morego 30, Genoa 16163, Italy; Department of Naval, Electrical, Electronic and Telecommunications Engineering University of Genova, Via All'Opera Pia 11 A, Genoa 16145, Italy
| | - Rosa Sicilia
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Via Alvaro del Portillo 21, Rome 00128, Italy
| | - Ermanno Cordelli
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Via Alvaro del Portillo 21, Rome 00128, Italy
| | - Deborah Fazzini
- Department of Diagnostic Imaging and Stereotactic Radiosurgey, Centro Diagnostico Italiano S.p.A., Via S. Saint Bon 20, Milan 20147, Italy
| | - Michaela Cellina
- Radiology Department, ASST Fatebenefratelli Sacco, Piazza Principessa Clotilde 3, Milan 20121, Italy
| | - Giancarlo Oliva
- Radiology Department, ASST Fatebenefratelli Sacco, Piazza Principessa Clotilde 3, Milan 20121, Italy
| | - Giovanni Callea
- Radiology Institute, Fondazione IRCCS Policlinico San Matteo, Viale Golgi 19, Pavia 27100, Italy
| | - Silvia Panella
- Diagnostic and interventional radiology unit, ASST Santi Paolo e Carlo - San Paolo Hospital, Via Antonio di Rudiní 8, Milan 20142, Italy
| | - Maurizio Cariati
- Department of Advanced Diagnostic Technologies - Therapeutic, Diagnostic and Radiology Units, ASST Santi Paolo e Carlo - San Paolo Hospital, Via Antonio di Rudiní 8, Milan 20142, Italy
| | - Diletta Cozzi
- Department of Emergency Radiology, Careggi University Hospital, Largo Piero Palagi 1, Florence 50139, Italy
| | - Vittorio Miele
- Department of Emergency Radiology, Careggi University Hospital, Largo Piero Palagi 1, Florence 50139, Italy
| | - Elvira Stellato
- Postgraduation School in Radiodiagnostics, Universitá degli Studi di Milano, Via Festa del Perdono, 7, Milan 20122, Italy
| | - Gianpaolo Carrafiello
- Operative Unit of Radiology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico of Milan, Via della Commenda, 10, Milan 20122, Italy; Department of Health Sciences, Univeristy of Milan, Via Festa del Perdono, 7, Milan 20122, Italy
| | - Giulia Castorani
- Diagnostic Imaging, Postgraduate Medical School, University of Foggia, Via Antonio Gramsci 89, Foggia 71122, Italy
| | - Annalisa Simeone
- Department of Diagnostic Imaging, IRCCS Ospedale Casa Sollievo della Sofferenza, Viale Cappuccini 1, San Giovanni Rotondo 71013, Italy
| | - Lorenzo Preda
- Radiology Institute, Fondazione IRCCS Policlinico San Matteo, Viale Golgi 19, Pavia 27100, Italy; Radiology Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Corso Str. Nuova, 65, Pavia 27100 Italy
| | - Giulio Iannello
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Via Alvaro del Portillo 21, Rome 00128, Italy
| | - Alessio Del Bue
- Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, Via Morego 30, Genoa 16163, Italy
| | - Fabio Tedoldi
- Bracco Imaging S.p.A., Via Caduti di Marcinelle 13, Milan 20134, Italy
| | - Marco Alí
- Department of Diagnostic Imaging and Stereotactic Radiosurgey, Centro Diagnostico Italiano S.p.A., Via S. Saint Bon 20, Milan 20147, Italy; Bracco Imaging S.p.A., Via Caduti di Marcinelle 13, Milan 20134, Italy
| | - Diego Sona
- Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, Via Morego 30, Genoa 16163, Italy; Fondazione Bruno Kessler, Via Sommarive, 18, Trento 38123, Italy
| | - Sergio Papa
- Department of Diagnostic Imaging and Stereotactic Radiosurgey, Centro Diagnostico Italiano S.p.A., Via S. Saint Bon 20, Milan 20147, Italy
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Tabatabaie M, Sarrami AH, Didehdar M, Tasorian B, Shafaat O, Sotoudeh H. Accuracy of Machine Learning Models to Predict Mortality in COVID-19 Infection Using the Clinical and Laboratory Data at the Time of Admission. Cureus 2021; 13:e18768. [PMID: 34804648 PMCID: PMC8592290 DOI: 10.7759/cureus.18768] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/14/2021] [Indexed: 12/16/2022] Open
Abstract
Aim This study aimed to develop a predictive model to predict patients’ mortality with coronavirus disease 2019 (COVID-19) from the basic medical data on the first day of admission. Methods The medical data including the demographic, clinical, and laboratory features on the first day of admission of clinically diagnosed COVID-19 patients were documented. The outcome of patients was also recorded as discharge or death. Feature selection models were then implemented and different machine learning models were developed on top of the selected features to predict discharge or death. The trained models were then tested on the test dataset. Results A total of 520 patients were included in the training dataset. The feature selection demonstrated 22 features as the most powerful predictive features. Among different machine learning models, the naive Bayes demonstrated the best performance with an area under the curve of 0.85. The ensemble model of the naive Bayes and neural network combination had slightly better performance with an area under the curve of 0.86. The models had relatively the same performance on the test dataset. Conclusion Developing a predictive machine learning model based on the basic medical features on the first day of admission in COVID-19 infection is feasible with acceptable performance.
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Affiliation(s)
- Mohsen Tabatabaie
- Health Information Management, Arak University of Medical Sciences, Arak, IRN
| | | | - Mojtaba Didehdar
- Medical Parasitology and Mycology, Arak University of Medical Sciences, Arak, IRN
| | - Baharak Tasorian
- Internal Medicine, Arak University of Medical Sciences, Arak, IRN
| | - Omid Shafaat
- Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Houman Sotoudeh
- Radiology, University of Alabama at Birmingham School of Medicine, Birmingham, USA
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Abstract
Organ segmentation, chest radiograph classification, and lung and liver nodule detections are some of the popular artificial intelligence (AI) tasks in chest and abdominal radiology due to the wide availability of public datasets. AI algorithms have achieved performance comparable to humans in less time for several organ segmentation tasks, and some lesion detection and classification tasks. This article introduces the current published articles of AI applied to chest and abdominal radiology, including organ segmentation, lesion detection, classification, and predicting prognosis.
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Affiliation(s)
- Sungwon Lee
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224D, 10 Center Drive, Bethesda, MD 20892-1182, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C224D, 10 Center Drive, Bethesda, MD 20892-1182, USA.
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30
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Wang T, Chen Z, Shang Q, Ma C, Chen X, Xiao E. A Promising and Challenging Approach: Radiologists' Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19. Diagnostics (Basel) 2021; 11:diagnostics11101924. [PMID: 34679622 PMCID: PMC8534829 DOI: 10.3390/diagnostics11101924] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/10/2021] [Accepted: 10/14/2021] [Indexed: 12/23/2022] Open
Abstract
Chest X-rays (CXR) and computed tomography (CT) are the main medical imaging modalities used against the increased worldwide spread of the 2019 coronavirus disease (COVID-19) epidemic. Machine learning (ML) and artificial intelligence (AI) technology, based on medical imaging fully extracting and utilizing the hidden information in massive medical imaging data, have been used in COVID-19 research of disease diagnosis and classification, treatment decision-making, efficacy evaluation, and prognosis prediction. This review article describes the extensive research of medical image-based ML and AI methods in preventing and controlling COVID-19, and summarizes their characteristics, differences, and significance in terms of application direction, image collection, and algorithm improvement, from the perspective of radiologists. The limitations and challenges faced by these systems and technologies, such as generalization and robustness, are discussed to indicate future research directions.
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Affiliation(s)
- Tianming Wang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Zhu Chen
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Quanliang Shang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Cong Ma
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Xiangyu Chen
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Enhua Xiao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
- Molecular Imaging Research Center, Central South University, Changsha 410008, China
- Correspondence:
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31
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Hu R, Gan J, Zhu X, Liu T, Shi X. Multi-task multi-modality SVM for early COVID-19 Diagnosis using chest CT data. Inf Process Manag 2021; 59:102782. [PMID: 34629687 PMCID: PMC8487772 DOI: 10.1016/j.ipm.2021.102782] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/17/2021] [Accepted: 09/23/2021] [Indexed: 01/08/2023]
Abstract
In the early diagnosis of the Coronavirus disease (COVID-19), it is of great importance for either distinguishing severe cases from mild cases or predicting the conversion time that mild cases would possibly convert to severe cases. This study investigates both of them in a unified framework by exploring the problems such as slight appearance difference between mild cases and severe cases, the interpretability, the High Dimension and Low Sample Size (HDLSS) data, and the class imbalance. To this end, the proposed framework includes three steps: (1) feature extraction which first conducts the hierarchical segmentation on the chest Computed Tomography (CT) image data and then extracts multi-modality handcrafted features for each segment, aiming at capturing the slight appearance difference from different perspectives; (2) data augmentation which employs the over-sampling technique to augment the number of samples corresponding to the minority classes, aiming at investigating the class imbalance problem; and (3) joint construction of classification and regression by proposing a novel Multi-task Multi-modality Support Vector Machine (MM-SVM) method to solve the issue of the HDLSS data and achieve the interpretability. Experimental analysis on two synthetic and one real COVID-19 data set demonstrated that our proposed framework outperformed six state-of-the-art methods in terms of binary classification and regression performance.
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Affiliation(s)
- Rongyao Hu
- School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,Massey University Albany Campus, Auckland 0745, New Zealand
| | - Jiangzhang Gan
- School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,Massey University Albany Campus, Auckland 0745, New Zealand
| | - Xiaofeng Zhu
- School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,Massey University Albany Campus, Auckland 0745, New Zealand
| | - Tong Liu
- Massey University Albany Campus, Auckland 0745, New Zealand
| | - Xiaoshuang Shi
- School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
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Varghese BA, Shin H, Desai B, Gholamrezanezhad A, Lei X, Perkins M, Oberai A, Nanda N, Cen S, Duddalwar V. Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs. Br J Radiol 2021; 94:20210221. [PMID: 34520246 PMCID: PMC9328073 DOI: 10.1259/bjr.20210221] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Objectives For optimal utilization of healthcare resources, there is a critical need for early identification of COVID-19 patients at risk of poor prognosis as defined by the need for intensive unit care and mechanical ventilation. We tested the feasibility of chest X-ray (CXR)-based radiomics metrics to develop machine-learning algorithms for predicting patients with poor outcomes. Methods In this Institutional Review Board (IRB) approved, Health Insurance Portability and Accountability Act (HIPAA) compliant, retrospective study, we evaluated CXRs performed around the time of admission from 167 COVID-19 patients. Of the 167 patients, 68 (40.72%) required intensive care during their stay, 45 (26.95%) required intubation, and 25 (14.97%) died. Lung opacities were manually segmented using ITK-SNAP (open-source software). CaPTk (open-source software) was used to perform 2D radiomics analysis. Results Of all the algorithms considered, the AdaBoost classifier performed the best with AUC = 0.72 to predict the need for intubation, AUC = 0.71 to predict death, and AUC = 0.61 to predict the need for admission to the intensive care unit (ICU). AdaBoost had similar performance with ElasticNet in predicting the need for admission to ICU. Analysis of the key radiomic metrics that drive model prediction and performance showed the importance of first-order texture metrics compared to other radiomics panel metrics. Using a Venn-diagram analysis, two first-order texture metrics and one second-order texture metric that consistently played an important role in driving model performance in all three outcome predictions were identified. Conclusions: Considering the quantitative nature and reliability of radiomic metrics, they can be used prospectively as prognostic markers to individualize treatment plans for COVID-19 patients and also assist with healthcare resource management. Advances in knowledge We report on the performance of CXR-based imaging metrics extracted from RT-PCR positive COVID-19 patients at admission to develop machine-learning algorithms for predicting the need for ICU, the need for intubation, and mortality, respectively.
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Affiliation(s)
| | - Heeseop Shin
- Keck School of Medicine, University of Southern California, CA, USA
| | - Bhushan Desai
- Keck School of Medicine, University of Southern California, CA, USA
| | | | - Xiaomeng Lei
- Keck School of Medicine, University of Southern California, CA, USA
| | - Melissa Perkins
- Keck School of Medicine, University of Southern California, CA, USA
| | - Assad Oberai
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Neha Nanda
- Keck School of Medicine, University of Southern California, CA, USA
| | - Steven Cen
- Keck School of Medicine, University of Southern California, CA, USA
| | - Vinay Duddalwar
- Keck School of Medicine, University of Southern California, CA, USA
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Kundu R, Das R, Geem ZW, Han GT, Sarkar R. Pneumonia detection in chest X-ray images using an ensemble of deep learning models. PLoS One 2021; 16:e0256630. [PMID: 34492046 PMCID: PMC8423280 DOI: 10.1371/journal.pone.0256630] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 08/12/2021] [Indexed: 02/08/2023] Open
Abstract
Pneumonia is a respiratory infection caused by bacteria or viruses; it affects many individuals, especially in developing and underdeveloped nations, where high levels of pollution, unhygienic living conditions, and overcrowding are relatively common, together with inadequate medical infrastructure. Pneumonia causes pleural effusion, a condition in which fluids fill the lung, causing respiratory difficulty. Early diagnosis of pneumonia is crucial to ensure curative treatment and increase survival rates. Chest X-ray imaging is the most frequently used method for diagnosing pneumonia. However, the examination of chest X-rays is a challenging task and is prone to subjective variability. In this study, we developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We employed deep transfer learning to handle the scarcity of available data and designed an ensemble of three convolutional neural network models: GoogLeNet, ResNet-18, and DenseNet-121. A weighted average ensemble technique was adopted, wherein the weights assigned to the base learners were determined using a novel approach. The scores of four standard evaluation metrics, precision, recall, f1-score, and the area under the curve, are fused to form the weight vector, which in studies in the literature was frequently set experimentally, a method that is prone to error. The proposed approach was evaluated on two publicly available pneumonia X-ray datasets, provided by Kermany et al. and the Radiological Society of North America (RSNA), respectively, using a five-fold cross-validation scheme. The proposed method achieved accuracy rates of 98.81% and 86.85% and sensitivity rates of 98.80% and 87.02% on the Kermany and RSNA datasets, respectively. The results were superior to those of state-of-the-art methods and our method performed better than the widely used ensemble techniques. Statistical analyses on the datasets using McNemar’s and ANOVA tests showed the robustness of the approach. The codes for the proposed work are available at https://github.com/Rohit-Kundu/Ensemble-Pneumonia-Detection.
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Affiliation(s)
- Rohit Kundu
- Department of Electrical Engineering, Jadavpur University, Kolkata, India
| | - Ritacheta Das
- Department of Computer Science & Engineering, Jadavpur University, Kolkata, India
| | - Zong Woo Geem
- College of IT Convergence, Gachon University, Seongnam, South Korea
- * E-mail:
| | - Gi-Tae Han
- College of IT Convergence, Gachon University, Seongnam, South Korea
| | - Ram Sarkar
- Department of Computer Science & Engineering, Jadavpur University, Kolkata, India
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Chen HJ, Mao L, Chen Y, Yuan L, Wang F, Li X, Cai Q, Qiu J, Chen F. Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia. BMC Infect Dis 2021; 21:931. [PMID: 34496794 PMCID: PMC8424152 DOI: 10.1186/s12879-021-06614-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 08/24/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spreading coronavirus disease 2019 (COVID-19). METHODS In this retrospective study, a total of 326 chest CT exams from 134 patients (63 confirmed COVID-19 patients and 71 non-COVID-19 patients) were collected from January 20 to February 8, 2020. A semi-automatic segmentation procedure was used to delineate the volume of interest (VOI), and radiomic features were extracted. The Support Vector Machine (SVM) model was built on the combination of 4 groups of features, including radiomic features, traditional radiological features, quantifying features, and clinical features. By repeating cross-validation procedure, the performance on the time-independent testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RESULTS For the SVM model built on the combination of 4 groups of features (integrated model), the per-exam AUC was 0.925 (95% CI 0.856 to 0.994) for differentiating COVID-19 on the testing cohort, and the sensitivity and specificity were 0.816 (95% CI 0.651 to 0.917) and 0.923 (95% CI 0.621 to 0.996), respectively. As for the SVM models built on radiomic features, radiological features, quantifying features, and clinical features, individually, the AUC on the testing cohort reached 0.765, 0.818, 0.607, and 0.739, respectively, significantly lower than the integrated model, except for the radiomic model. CONCLUSION The machine learning-based CT radiomics models may accurately classify COVID-19, helping clinicians and radiologists to identify COVID-19 positive cases.
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Affiliation(s)
- Hui Juan Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China
| | - Li Mao
- Deepwise AI Lab, Deepwise Inc., No. 8 Haidian avenue, Sinosteel International Plaza, Beijing, 100080, China
| | - Yang Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China
| | - Li Yuan
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China
| | - Fei Wang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China
| | - Xiuli Li
- Deepwise AI Lab, Deepwise Inc., No. 8 Haidian avenue, Sinosteel International Plaza, Beijing, 100080, China
| | - Qinlei Cai
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China
| | - Jie Qiu
- Department of Ultrasound, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China.
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Abstract
Automatic segmentation of infection areas in computed tomography (CT) images has proven to be an effective diagnostic approach for COVID-19. However, due to the limited number of pixel-level annotated medical images, accurate segmentation remains a major challenge. In this paper, we propose an unsupervised domain adaptation based segmentation network to improve the segmentation performance of the infection areas in COVID-19 CT images. In particular, we propose to utilize the synthetic data and limited unlabeled real COVID-19 CT images to jointly train the segmentation network. Furthermore, we develop a novel domain adaptation module, which is used to align the two domains and effectively improve the segmentation network's generalization capability to the real domain. Besides, we propose an unsupervised adversarial training scheme, which encourages the segmentation network to learn the domain-invariant feature, so that the robust feature can be used for segmentation. Experimental results demonstrate that our method can achieve state-of-the-art segmentation performance on COVID-19 CT images.
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36
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Bottino F, Tagliente E, Pasquini L, Napoli AD, Lucignani M, Figà-Talamanca L, Napolitano A. COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal. J Pers Med 2021; 11:893. [PMID: 34575670 PMCID: PMC8467935 DOI: 10.3390/jpm11090893] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 08/26/2021] [Accepted: 09/03/2021] [Indexed: 12/21/2022] Open
Abstract
More than a year has passed since the report of the first case of coronavirus disease 2019 (COVID), and increasing deaths continue to occur. Minimizing the time required for resource allocation and clinical decision making, such as triage, choice of ventilation modes and admission to the intensive care unit is important. Machine learning techniques are acquiring an increasingly sought-after role in predicting the outcome of COVID patients. Particularly, the use of baseline machine learning techniques is rapidly developing in COVID mortality prediction, since a mortality prediction model could rapidly and effectively help clinical decision-making for COVID patients at imminent risk of death. Recent studies reviewed predictive models for SARS-CoV-2 diagnosis, severity, length of hospital stay, intensive care unit admission or mechanical ventilation modes outcomes; however, systematic reviews focused on prediction of COVID mortality outcome with machine learning methods are lacking in the literature. The present review looked into the studies that implemented machine learning, including deep learning, methods in COVID mortality prediction thus trying to present the existing published literature and to provide possible explanations of the best results that the studies obtained. The study also discussed challenging aspects of current studies, providing suggestions for future developments.
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Affiliation(s)
- Francesca Bottino
- Medical Physics Department Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), 00165 Rome, Italy;
| | - Emanuela Tagliente
- Medical Physics Department Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), 00165 Rome, Italy;
| | - Luca Pasquini
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, 00165 Rome, Italy; (L.P.); (A.D.N.)
- Neuroradiology Service, Radiology Department, Memorial Sloan Kettering Cancer Center, New York, NY 1275, USA
| | - Alberto Di Napoli
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, 00165 Rome, Italy; (L.P.); (A.D.N.)
- Radiology Department, Castelli Romani Hospital, 00040 Ariccia (RM), Italy
| | - Martina Lucignani
- Medical Physics Department Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), 00165 Rome, Italy;
| | - Lorenzo Figà-Talamanca
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), 00165 Rome, Italy;
| | - Antonio Napolitano
- Medical Physics Department Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), 00165 Rome, Italy;
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37
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Shakeel SM, Kumar NS, Madalli PP, Srinivasaiah R, Swamy DR. COVID-19 prediction models: a systematic literature review. Osong Public Health Res Perspect 2021; 12:215-229. [PMID: 34465071 PMCID: PMC8408413 DOI: 10.24171/j.phrp.2021.0100] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 07/12/2021] [Indexed: 12/24/2022] Open
Abstract
As the world grapples with the problem of the coronavirus disease 2019 (COVID-19) pandemic and its devastating effects, scientific groups are working towards solutions to mitigate the effects of the virus. This paper aimed to collate information on COVID-19 prediction models. A systematic literature review is reported, based on a manual search of 1,196 papers published from January to December 2020. Various databases such as Google Scholar, Web of Science, and Scopus were searched. The search strategy was formulated and refined in terms of subject keywords, geographical purview, and time period according to a predefined protocol. Visualizations were created to present the data trends according to different parameters. The results of this systematic literature review show that the study findings are critically relevant for both healthcare managers and prediction model developers. Healthcare managers can choose the best prediction model output for their organization or process management. Meanwhile, prediction model developers and managers can identify the lacunae in their models and improve their data-driven approaches.
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Affiliation(s)
- Sheikh Muzaffar Shakeel
- Department of Industrial Engineering and Management, JSS Academy of Technical Education, Bengaluru, India
| | - Nithya Sathya Kumar
- Department of Industrial Engineering and Management, JSS Academy of Technical Education, Bengaluru, India
| | - Pranita Pandurang Madalli
- Department of Industrial Engineering and Management, JSS Academy of Technical Education, Bengaluru, India
| | - Rashmi Srinivasaiah
- Department of Industrial Engineering and Management, JSS Academy of Technical Education, Bengaluru, India
| | - Devappa Renuka Swamy
- Department of Industrial Engineering and Management, JSS Academy of Technical Education, Bengaluru, India
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38
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Aznar-Gimeno R, Esteban LM, Labata-Lezaun G, del-Hoyo-Alonso R, Abadia-Gallego D, Paño-Pardo JR, Esquillor-Rodrigo MJ, Lanas Á, Serrano MT. A Clinical Decision Web to Predict ICU Admission or Death for Patients Hospitalised with COVID-19 Using Machine Learning Algorithms. Int J Environ Res Public Health 2021; 18:8677. [PMID: 34444425 PMCID: PMC8394359 DOI: 10.3390/ijerph18168677] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/09/2021] [Accepted: 08/11/2021] [Indexed: 12/24/2022]
Abstract
The purpose of the study was to build a predictive model for estimating the risk of ICU admission or mortality among patients hospitalized with COVID-19 and provide a user-friendly tool to assist clinicians in the decision-making process. The study cohort comprised 3623 patients with confirmed COVID-19 who were hospitalized in the SALUD hospital network of Aragon (Spain), which includes 23 hospitals, between February 2020 and January 2021, a period that includes several pandemic waves. Up to 165 variables were analysed, including demographics, comorbidity, chronic drugs, vital signs, and laboratory data. To build the predictive models, different techniques and machine learning (ML) algorithms were explored: multilayer perceptron, random forest, and extreme gradient boosting (XGBoost). A reduction dimensionality procedure was used to minimize the features to 20, ensuring feasible use of the tool in practice. Our model was validated both internally and externally. We also assessed its calibration and provide an analysis of the optimal cut-off points depending on the metric to be optimized. The best performing algorithm was XGBoost. The final model achieved good discrimination for the external validation set (AUC = 0.821, 95% CI 0.787-0.854) and accurate calibration (slope = 1, intercept = -0.12). A cut-off of 0.4 provides a sensitivity and specificity of 0.71 and 0.78, respectively. In conclusion, we built a risk prediction model from a large amount of data from several pandemic waves, which had good calibration and discrimination ability. We also created a user-friendly web application that can aid rapid decision-making in clinical practice.
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Affiliation(s)
- Rocío Aznar-Gimeno
- Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain; (G.L.-L.); (D.A.-G.)
| | - Luis M. Esteban
- Escuela Universitaria Politécnica de La Almunia, Universidad de Zaragoza, Calle Mayor, 5, 50100 La Almunia de Doña Godina, Spain;
| | - Gorka Labata-Lezaun
- Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain; (G.L.-L.); (D.A.-G.)
| | - Rafael del-Hoyo-Alonso
- Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain; (G.L.-L.); (D.A.-G.)
| | - David Abadia-Gallego
- Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain; (G.L.-L.); (D.A.-G.)
| | - J. Ramón Paño-Pardo
- Infectious Disease Department, University Clinic Hospital Lozano Blesa, San Juan Bosco 15, 50009 Zaragoza, Spain;
- Department of Medicine, Psychiatry and Dermatology, University of Zaragoza, 50009 Zaragoza, Spain; (Á.L.); (M.T.S.)
- Aragon Health Research Institute (IIS Aragon), 50009 Zaragoza, Spain
| | - M. José Esquillor-Rodrigo
- Internal Medicine Department, University Clinic Hospital Lozano Blesa, San Juan Bosco 15, 50009 Zaragoza, Spain;
| | - Ángel Lanas
- Department of Medicine, Psychiatry and Dermatology, University of Zaragoza, 50009 Zaragoza, Spain; (Á.L.); (M.T.S.)
- Aragon Health Research Institute (IIS Aragon), 50009 Zaragoza, Spain
- Digestive Diseases Department, University Clinic Hospital Lozano Blesa, San Juan Bosco 15, 50009 Zaragoza, Spain
- CIBEREHD, 28029 Madrid, Spain
| | - M. Trinidad Serrano
- Department of Medicine, Psychiatry and Dermatology, University of Zaragoza, 50009 Zaragoza, Spain; (Á.L.); (M.T.S.)
- Aragon Health Research Institute (IIS Aragon), 50009 Zaragoza, Spain
- Digestive Diseases Department, University Clinic Hospital Lozano Blesa, San Juan Bosco 15, 50009 Zaragoza, Spain
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39
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Wang J, Liu C, Li J, Yuan C, Zhang L, Jin C, Xu J, Wang Y, Wen Y, Lu H, Li B, Chen C, Li X, Shen D, Qian D, Wang J. iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients. NPJ Digit Med 2021; 4:124. [PMID: 34400751 PMCID: PMC8367981 DOI: 10.1038/s41746-021-00496-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 07/21/2021] [Indexed: 02/07/2023] Open
Abstract
Most prior studies focused on developing models for the severity or mortality prediction of COVID-19 patients. However, effective models for recovery-time prediction are still lacking. Here, we present a deep learning solution named iCOVID that can successfully predict the recovery-time of COVID-19 patients based on predefined treatment schemes and heterogeneous multimodal patient information collected within 48 hours after admission. Meanwhile, an interpretable mechanism termed FSR is integrated into iCOVID to reveal the features greatly affecting the prediction of each patient. Data from a total of 3008 patients were collected from three hospitals in Wuhan, China, for large-scale verification. The experiments demonstrate that iCOVID can achieve a time-dependent concordance index of 74.9% (95% CI: 73.6-76.3%) and an average day error of 4.4 days (95% CI: 4.2-4.6 days). Our study reveals that treatment schemes, age, symptoms, comorbidities, and biomarkers are highly related to recovery-time predictions.
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Affiliation(s)
- Jun Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Jingwen Li
- Department of Gastroenterology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Cheng Yuan
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Lichi Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Cheng Jin
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jianwei Xu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yaqi Wang
- College of Media, Communication University of Zhejiang, Hangzhou, China
| | - Yaofeng Wen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Hongbing Lu
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai, China
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiangdong Li
- Department of Radiology, General Hospital of Southern Theatre Command, PLA, Guangzhou, China.
- Department of Radiology, Huoshenshan Hospital, Wuhan, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd, Shanghai, China.
| | - Dahong Qian
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai, China.
| | - Jian Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China.
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40
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Özcan ANŞ, Aslan K. Diagnostic accuracy of sagittal TSE-T2W, variable flip angle 3D TSE-T2W and high-resolution 3D heavily T2W sequences for the stenosis of two localizations: the cerebral aqueduct and the superior medullary velum. Curr Med Imaging 2021; 17:1432-1438. [PMID: 34365953 DOI: 10.2174/1573405617666210806123720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 04/07/2021] [Accepted: 05/03/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVES This study aimed to investigate the accuracy of conventional sagittal turbo spin echo T2-weighted (Sag TSE-T2W), variable flip angle 3D TSE (VFA-3D-TSE) and high-resolution 3D heavily T2W (HR-3D-HT2W) sequences in the diagnosis of primary aqueductal stenosis (PAS) and superior medullary velum stenosis (SMV-S), and the effect of stenosis localization on diagnosis. METHODS Seventy-seven patients were included in the study. The diagnosis accuracy of the HR-3D-HT2W, Sag TSE-T2W and VFA-3D-TSE sequences, was classified into three grades by two experienced neuroradiologists: grade 0 (the sequence has no diagnostic ability), grade 1 (the sequence diagnoses stenosis but does not show focal stenosis itself or membrane formation), and grade 2 (the sequence makes a definitive diagnosis of stenosis and shows focal stenosis itself or membrane formation). Stenosis localizations were divided into three as Cerebral Aquaduct (CA), superior medullary velum (SMV) and SMV+CA. In the statistical analysis, the grades of the sequences were compared without making a differentiation based on localization. Then, the effect of localization on diagnosis was determined by comparing the grades for individual localizations. RESULTS In the sequence comparison, grade 0 was not detected in the VFA-3D-TSE and HR-3D-HT2W sequences, and these sequences diagnosed all cases. On the other hand, 25.4% of grade 0 was detected with the Sag TSE-T2W sequence (P<0.05). Grade 1 was detected by VFA-3D-TSE in 23% of the cases, while grade 1 (12.5%) was detected by HRH-3D-T2W in only one case, and the difference was statistically significant (P<0.05). When the sequences were examined according to localizations, the rate of grade 0 in the Sag TSE-T2W sequence was statistically significantly higher for the SMV localization (33.3%) compared to CA (66.7%) and SMV+CA (0%) (P<0.05). Localization had no effect on diagnosis using the other sequences. CONCLUSION In our study, we found that the VFA-3D-TSE and HR-3D-HT2W sequences were successful in the diagnosis of PAS and SMV-S contrary to the Sag TSE-T2W sequence.
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Affiliation(s)
| | - Kerim Aslan
- Samsun Ondokuz Mayıs University, Department of Radiology, Samsun. Turkey
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41
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Wang S, Dong D, Li L, Li H, Bai Y, Hu Y, Huang Y, Yu X, Liu S, Qiu X, Lu L, Wang M, Zha Y, Tian J. A Deep Learning Radiomics Model to Identify Poor Outcome in COVID-19 Patients With Underlying Health Conditions: A Multicenter Study. IEEE J Biomed Health Inform 2021; 25:2353-2362. [PMID: 33905341 PMCID: PMC8545077 DOI: 10.1109/jbhi.2021.3076086] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Objective: Coronavirus disease 2019 (COVID-19) has caused considerable morbidity and mortality, especially in patients with underlying health conditions. A precise prognostic tool to identify poor outcomes among such cases is desperately needed. Methods: Total 400 COVID-19 patients with underlying health conditions were retrospectively recruited from 4 centers, including 54 dead cases (labeled as poor outcomes) and 346 patients discharged or hospitalized for at least 7 days since initial CT scan. Patients were allocated to a training set (n = 271), a test set (n = 68), and an external test set (n = 61). We proposed an initial CT-derived hybrid model by combining a 3D-ResNet10 based deep learning model and a quantitative 3D radiomics model to predict the probability of COVID-19 patients reaching poor outcome. The model performance was assessed by area under the receiver operating characteristic curve (AUC), survival analysis, and subgroup analysis. Results: The hybrid model achieved AUCs of 0.876 (95% confidence interval: 0.752-0.999) and 0.864 (0.766-0.962) in test and external test sets, outperforming other models. The survival analysis verified the hybrid model as a significant risk factor for mortality (hazard ratio, 2.049 [1.462–2.871], P < 0.001) that could well stratify patients into high-risk and low-risk of reaching poor outcomes (P < 0.001). Conclusion: The hybrid model that combined deep learning and radiomics could accurately identify poor outcomes in COVID-19 patients with underlying health conditions from initial CT scans. The great risk stratification ability could help alert risk of death and allow for timely surveillance plans.
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42
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Guiot J, Vaidyanathan A, Deprez L, Zerka F, Danthine D, Frix AN, Lambin P, Bottari F, Tsoutzidis N, Miraglio B, Walsh S, Vos W, Hustinx R, Ferreira M, Lovinfosse P, Leijenaar RTH. A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev 2021; 42:426-440. [PMID: 34309893 DOI: 10.1002/med.21846] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 07/05/2021] [Accepted: 07/07/2021] [Indexed: 12/14/2022]
Abstract
Radiomics is the quantitative analysis of standard-of-care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand-crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine. Radiomics exploits sophisticated image analysis tools coupled with statistical elaboration to extract the wealth of information hidden inside medical images, such as computed tomography (CT), magnetic resonance (MR), and/or Positron emission tomography (PET) scans, routinely performed in the everyday clinical practice. Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts. However, the booming of publications and commercial applications of radiomics approaches warrant caution and proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision-making, from oncology and respiratory medicine to pharmacological and genotyping studies.
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Affiliation(s)
- Julien Guiot
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Akshayaa Vaidyanathan
- Radiomics (Oncoradiomics SA), Liège, Belgium.,The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Louis Deprez
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Fadila Zerka
- Radiomics (Oncoradiomics SA), Liège, Belgium.,The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Denis Danthine
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Anne-Noelle Frix
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | | | | | | | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Wim Vos
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Roland Hustinx
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liege, Liege, Belgium.,GIGA-CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Marta Ferreira
- GIGA-CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Pierre Lovinfosse
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liege, Liege, Belgium
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43
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Laino ME, Ammirabile A, Posa A, Cancian P, Shalaby S, Savevski V, Neri E. The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review. Diagnostics (Basel) 2021; 11:1317. [PMID: 34441252 PMCID: PMC8394327 DOI: 10.3390/diagnostics11081317] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 07/02/2021] [Accepted: 07/09/2021] [Indexed: 12/23/2022] Open
Abstract
Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.
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Affiliation(s)
- Maria Elena Laino
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy; (P.C.); (V.S.)
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy;
- Department of Radiology, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Alessandro Posa
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario Agostino Gemelli—IRCCS, 00168 Rome, Italy;
| | - Pierandrea Cancian
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy; (P.C.); (V.S.)
| | - Sherif Shalaby
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Via Roma 67, 56126 Pisa, Italy; (S.S.); (E.N.)
| | - Victor Savevski
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy; (P.C.); (V.S.)
| | - Emanuele Neri
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Via Roma 67, 56126 Pisa, Italy; (S.S.); (E.N.)
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, Via della Signora 2, 20122 Milano, Italy
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44
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Yousefi B, Kawakita S, Amini A, Akbari H, Advani SM, Akhloufi M, Maldague XPV, Ahadian S. Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics. J Clin Med 2021; 10:3100. [PMID: 34300266 PMCID: PMC8304336 DOI: 10.3390/jcm10143100] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 07/01/2021] [Accepted: 07/07/2021] [Indexed: 12/31/2022] Open
Abstract
The COVID-19 pandemic continues to spread globally at a rapid pace, and its rapid detection remains a challenge due to its rapid infectivity and limited testing availability. One of the simply available imaging modalities in clinical routine involves chest X-ray (CXR), which is often used for diagnostic purposes. Here, we proposed a computer-aided detection of COVID-19 in CXR imaging using deep and conventional radiomic features. First, we used a 2D U-Net model to segment the lung lobes. Then, we extracted deep latent space radiomics by applying deep convolutional autoencoder (ConvAE) with internal dense layers to extract low-dimensional deep radiomics. We used Johnson-Lindenstrauss (JL) lemma, Laplacian scoring (LS), and principal component analysis (PCA) to reduce dimensionality in conventional radiomics. The generated low-dimensional deep and conventional radiomics were integrated to classify COVID-19 from pneumonia and healthy patients. We used 704 CXR images for training the entire model (i.e., U-Net, ConvAE, and feature selection in conventional radiomics). Afterward, we independently validated the whole system using a study cohort of 1597 cases. We trained and tested a random forest model for detecting COVID-19 cases through multivariate binary-class and multiclass classification. The maximal (full multivariate) model using a combination of the two radiomic groups yields performance in classification cross-validated accuracy of 72.6% (69.4-74.4%) for multiclass and 89.6% (88.4-90.7%) for binary-class classification.
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Affiliation(s)
- Bardia Yousefi
- Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Satoru Kawakita
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA 90024, USA; (S.K.); (S.M.A.)
| | - Arya Amini
- Department of Radiation Oncology, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA;
| | - Hamed Akbari
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Shailesh M. Advani
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA 90024, USA; (S.K.); (S.M.A.)
| | - Moulay Akhloufi
- Department of Computer Science, Perception Robotics and Intelligent Machines (PRIME) Research Group, University of Moncton, New Brunswick, NB E1A 3E9, Canada;
| | - Xavier P. V. Maldague
- Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Samad Ahadian
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA 90024, USA; (S.K.); (S.M.A.)
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Mondal MRH, Bharati S, Podder P. Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A Review. Curr Med Imaging 2021; 17:1403-1418. [PMID: 34259149 DOI: 10.2174/1573405617666210713113439] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 03/29/2021] [Accepted: 04/08/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND This paper provides a systematic review of the application of artificial intelligence (AI) in the form of machine learning (ML) and deep learning (DL) techniques in fighting against the effects of novel coronavirus disease (COVID-19). OBJECTIVE & METHOD The objective is to perform a scoping review on AI for COVID-19 using preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. A literature search was performed for relevant studies published from 1 January 2020 till 27 March 2021. Out of 4050 research papers available in reputed publishers, a full-text review of 440 articles was done based on the keywords of AI, COVID-19, ML, forecasting, DL, X-ray, and computed tomography (CT). Finally, 52 articles were included in the result synthesis of this paper. As part of the review, different ML regression methods were reviewed first in predicting the number of confirmed and death cases. Secondly, a comprehensive survey was carried out on the use of ML in classifying COVID-19 patients. Thirdly, different datasets on medical imaging were compared in terms of the number of images, number of positive samples and number of classes in the datasets. The different stages of the diagnosis, including preprocessing, segmentation and feature extraction were also reviewed. Fourthly, the performance results of different research papers were compared to evaluate the effectiveness of DL methods on different datasets. RESULTS Results show that residual neural network (ResNet-18) and densely connected convolutional network (DenseNet 169) exhibit excellent classification accuracy for X-ray images, while DenseNet-201 has the maximum accuracy in classifying CT scan images. This indicates that ML and DL are useful tools in assisting researchers and medical professionals in predicting, screening and detecting COVID-19. CONCLUSION Finally, this review highlights the existing challenges, including regulations, noisy data, data privacy, and the lack of reliable large datasets, then provides future research directions in applying AI in managing COVID-19.
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Affiliation(s)
| | - Subrato Bharati
- Institute of ICT, Bangladesh University of Engineering and Technology, Dhaka-1205, Bangladesh
| | - Prajoy Podder
- Institute of ICT, Bangladesh University of Engineering and Technology, Dhaka-1205, Bangladesh
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Dogan O, Tiwari S, Jabbar MA, Guggari S. A systematic review on AI/ML approaches against COVID-19 outbreak. COMPLEX INTELL SYST 2021; 7:2655-2678. [PMID: 34777970 PMCID: PMC8256231 DOI: 10.1007/s40747-021-00424-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 06/05/2021] [Indexed: 12/24/2022]
Abstract
A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.
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Affiliation(s)
- Onur Dogan
- Department of Industrial Engineering, Izmir Bakircay University, 35665 Izmir, Turkey.,Research Center for Data Analytics and Spatial Data Modeling (RC-DAS), Izmir Bakircay University, 35665 Izmir, Turkey
| | - Sanju Tiwari
- Department of Computer Science, Universidad Autonoma de Tamaulipas, Ciudad Victoria, Mexico
| | - M A Jabbar
- Vardhaman College of Engineering, Kacharam, India
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47
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Gong K, Wu D, Arru CD, Homayounieh F, Neumark N, Guan J, Buch V, Kim K, Bizzo BC, Ren H, Tak WY, Park SY, Lee YR, Kang MK, Park JG, Carriero A, Saba L, Masjedi M, Talari H, Babaei R, Mobin HK, Ebrahimian S, Guo N, Digumarthy SR, Dayan I, Kalra MK, Li Q. A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records. Eur J Radiol 2021; 139:109583. [PMID: 33846041 PMCID: PMC7863774 DOI: 10.1016/j.ejrad.2021.109583] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 01/28/2021] [Accepted: 02/01/2021] [Indexed: 12/31/2022]
Abstract
PURPOSE As of August 30th, there were in total 25.1 million confirmed cases and 845 thousand deaths caused by coronavirus disease of 2019 (COVID-19) worldwide. With overwhelming demands on medical resources, patient stratification based on their risks is essential. In this multi-center study, we built prognosis models to predict severity outcomes, combining patients' electronic health records (EHR), which included vital signs and laboratory data, with deep learning- and CT-based severity prediction. METHOD We first developed a CT segmentation network using datasets from multiple institutions worldwide. Two biomarkers were extracted from the CT images: total opacity ratio (TOR) and consolidation ratio (CR). After obtaining TOR and CR, further prognosis analysis was conducted on datasets from INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3. For each data cohort, generalized linear model (GLM) was applied for prognosis prediction. RESULTS For the deep learning model, the correlation coefficient of the network prediction and manual segmentation was 0.755, 0.919, and 0.824 for the three cohorts, respectively. The AUC (95 % CI) of the final prognosis models was 0.85(0.77,0.92), 0.93(0.87,0.98), and 0.86(0.75,0.94) for INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3 cohorts, respectively. Either TOR or CR exist in all three final prognosis models. Age, white blood cell (WBC), and platelet (PLT) were chosen predictors in two cohorts. Oxygen saturation (SpO2) was a chosen predictor in one cohort. CONCLUSION The developed deep learning method can segment lung infection regions. Prognosis results indicated that age, SpO2, CT biomarkers, PLT, and WBC were the most important prognostic predictors of COVID-19 in our prognosis model.
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Affiliation(s)
- Kuang Gong
- Department of Radiology, Massachusetts General Hospital, Boston, United States
| | - Dufan Wu
- Department of Radiology, Massachusetts General Hospital, Boston, United States
| | - Chiara Daniela Arru
- Department of Radiology, Massachusetts General Hospital, Boston, United States
| | - Fatemeh Homayounieh
- Department of Radiology, Massachusetts General Hospital, Boston, United States
| | - Nir Neumark
- MGH & BWH Center for Clinical Data Science, Boston, United States
| | | | - Varun Buch
- MGH & BWH Center for Clinical Data Science, Boston, United States
| | - Kyungsang Kim
- Department of Radiology, Massachusetts General Hospital, Boston, United States
| | | | - Hui Ren
- Department of Radiology, Massachusetts General Hospital, Boston, United States
| | - Won Young Tak
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Soo Young Park
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Yu Rim Lee
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Min Kyu Kang
- Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, South Korea
| | - Jung Gil Park
- Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, South Korea
| | - Alessandro Carriero
- Radiologia, Azienda Ospedaliera Universitaria Maggiore della Carità, Novara, Italy
| | - Luca Saba
- Radiologia, Azienda Ospedaliera Universitaria Policlinico di Monserrato, Italy
| | - Mahsa Masjedi
- Department of Radiology, Kashan University of Medical Sciences, Kashan, Iran
| | - Hamidreza Talari
- Department of Radiology, Kashan University of Medical Sciences, Kashan, Iran
| | - Rosa Babaei
- Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Hadi Karimi Mobin
- Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Shadi Ebrahimian
- Department of Radiology, Massachusetts General Hospital, Boston, United States
| | - Ning Guo
- Department of Radiology, Massachusetts General Hospital, Boston, United States
| | - Subba R Digumarthy
- Department of Radiology, Massachusetts General Hospital, Boston, United States
| | - Ittai Dayan
- MGH & BWH Center for Clinical Data Science, Boston, United States
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, United States.
| | - Quanzheng Li
- Department of Radiology, Massachusetts General Hospital, Boston, United States.
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Kao YS, Lin KT. A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia. Diagnostics (Basel) 2021; 11:991. [PMID: 34072573 PMCID: PMC8229671 DOI: 10.3390/diagnostics11060991] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 05/28/2021] [Accepted: 05/28/2021] [Indexed: 12/26/2022] Open
Abstract
INTRODUCTION Coronavirus disease 2019 (COVID-19) led to a global pandemic. Although reverse transcription polymerase chain reaction (RT-PCR) of viral nucleic acid is the gold standard for COVID-19 diagnosis, its sensitivity was found to not be high enough in many reports. As radiomics-based diagnosis research has recently emerged, we aimed to use computerized tomography (CT)-based radiomics models to differentiate COVID-19 pneumonia from other viral pneumonia infections. MATERIALS AND METHODS This study was performed according to the preferred reporting items for systematic review and meta-analysis diagnostic test accuracy studies (PRISMA-DTA) guidelines. The Pubmed, Cochrane, and Embase databases were searched. The pooled sensitivity and pooled specificity were calculated. A summary receiver operating characteristic (sROC) curve was constructed. The study quality was evaluated based on the radiomics quality score. RESULTS A total of 10,300 patients were involved in this meta-analysis. The radiomics quality score ranged from 13 to 16 (maximum score: 36). The pooled sensitivity was 0.885 (95% CI: 0.818-0.929), and the pooled specificity was 0.811 (95% CI: 0.667-0.902). The pooled AUC was 906. Conclusion: Our meta-analysis showed that CT-based radiomics feature models can successfully differentiate COVID-19 from other viral pneumonias.
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Affiliation(s)
- Yung-Shuo Kao
- Department of Radiation Oncology, China Medical University Hospital, Taichung 404, Taiwan;
| | - Kun-Te Lin
- Department of Emergency Medicine, Changhua Christian Hospital, Changhua 500, Taiwan
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Kumar V, Singh D, Kaur M, Damaševičius R. Overview of current state of research on the application of artificial intelligence techniques for COVID-19. PeerJ Comput Sci 2021; 7:e564. [PMID: 34141890 PMCID: PMC8176528 DOI: 10.7717/peerj-cs.564] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 05/05/2021] [Indexed: 05/09/2023]
Abstract
BACKGROUND Until now, there are still a limited number of resources available to predict and diagnose COVID-19 disease. The design of novel drug-drug interaction for COVID-19 patients is an open area of research. Also, the development of the COVID-19 rapid testing kits is still a challenging task. METHODOLOGY This review focuses on two prime challenges caused by urgent needs to effectively address the challenges of the COVID-19 pandemic, i.e., the development of COVID-19 classification tools and drug discovery models for COVID-19 infected patients with the help of artificial intelligence (AI) based techniques such as machine learning and deep learning models. RESULTS In this paper, various AI-based techniques are studied and evaluated by the means of applying these techniques for the prediction and diagnosis of COVID-19 disease. This study provides recommendations for future research and facilitates knowledge collection and formation on the application of the AI techniques for dealing with the COVID-19 epidemic and its consequences. CONCLUSIONS The AI techniques can be an effective tool to tackle the epidemic caused by COVID-19. These may be utilized in four main fields such as prediction, diagnosis, drug design, and analyzing social implications for COVID-19 infected patients.
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Affiliation(s)
- Vijay Kumar
- Computer Science and Engineering Department, National Institute of Technology, Hamirpur, Himachal Pradesh, India
| | - Dilbag Singh
- School of Engineering and Applied Sciences, Bennett University, Greater Noida, India
| | - Manjit Kaur
- School of Engineering and Applied Sciences, Bennett University, Greater Noida, India
| | - Robertas Damaševičius
- Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland
- Department of Applied Informatics, Vytautas Magnus University, Kaunas, Lithuania
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50
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Abdulkareem M, Petersen SE. The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype. Front Artif Intell 2021; 4:652669. [PMID: 34056579 PMCID: PMC8160471 DOI: 10.3389/frai.2021.652669] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/13/2021] [Indexed: 12/24/2022] Open
Abstract
COVID-19 has created enormous suffering, affecting lives, and causing deaths. The ease with which this type of coronavirus can spread has exposed weaknesses of many healthcare systems around the world. Since its emergence, many governments, research communities, commercial enterprises, and other institutions and stakeholders around the world have been fighting in various ways to curb the spread of the disease. Science and technology have helped in the implementation of policies of many governments that are directed toward mitigating the impacts of the pandemic and in diagnosing and providing care for the disease. Recent technological tools, artificial intelligence (AI) tools in particular, have also been explored to track the spread of the coronavirus, identify patients with high mortality risk and diagnose patients for the disease. In this paper, areas where AI techniques are being used in the detection, diagnosis and epidemiological predictions, forecasting and social control for combating COVID-19 are discussed, highlighting areas of successful applications and underscoring issues that need to be addressed to achieve significant progress in battling COVID-19 and future pandemics. Several AI systems have been developed for diagnosing COVID-19 using medical imaging modalities such as chest CT and X-ray images. These AI systems mainly differ in their choices of the algorithms for image segmentation, classification and disease diagnosis. Other AI-based systems have focused on predicting mortality rate, long-term patient hospitalization and patient outcomes for COVID-19. AI has huge potential in the battle against the COVID-19 pandemic but successful practical deployments of these AI-based tools have so far been limited due to challenges such as limited data accessibility, the need for external evaluation of AI models, the lack of awareness of AI experts of the regulatory landscape governing the deployment of AI tools in healthcare, the need for clinicians and other experts to work with AI experts in a multidisciplinary context and the need to address public concerns over data collection, privacy, and protection. Having a dedicated team with expertise in medical data collection, privacy, access and sharing, using federated learning whereby AI scientists hand over training algorithms to the healthcare institutions to train models locally, and taking full advantage of biomedical data stored in biobanks can alleviate some of problems posed by these challenges. Addressing these challenges will ultimately accelerate the translation of AI research into practical and useful solutions for combating pandemics.
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Affiliation(s)
- Musa Abdulkareem
- Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
| | - Steffen E. Petersen
- Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- The Alan Turing Institute, London, United Kingdom
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