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Ankolekar A, Eppings L, Bottari F, Pinho IF, Howard K, Baker R, Nan Y, Xing X, Walsh SLF, Vos W, Yang G, Lambin P. Using artificial intelligence and predictive modelling to enable learning healthcare systems (LHS) for pandemic preparedness. Comput Struct Biotechnol J 2024; 24:412-419. [PMID: 38831762 PMCID: PMC11145382 DOI: 10.1016/j.csbj.2024.05.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 05/07/2024] [Accepted: 05/07/2024] [Indexed: 06/05/2024] Open
Abstract
In anticipation of potential future pandemics, we examined the challenges and opportunities presented by the COVID-19 outbreak. This analysis highlights how artificial intelligence (AI) and predictive models can support both patients and clinicians in managing subsequent infectious diseases, and how legislators and policymakers could support these efforts, to bring learning healthcare system (LHS) from guidelines to real-world implementation. This report chronicles the trajectory of the COVID-19 pandemic, emphasizing the diverse data sets generated throughout its course. We propose strategies for harnessing this data via AI and predictive modelling to enhance the functioning of LHS. The challenges faced by patients and healthcare systems around the world during this unprecedented crisis could have been mitigated with an informed and timely adoption of the three pillars of the LHS: Knowledge, Data and Practice. By harnessing AI and predictive analytics, we can develop tools that not only detect potential pandemic-prone diseases early on but also assist in patient management, provide decision support, offer treatment recommendations, deliver patient outcome triage, predict post-recovery long-term disease impacts, monitor viral mutations and variant emergence, and assess vaccine and treatment efficacy in real-time. A patient-centric approach remains paramount, ensuring patients are both informed and actively involved in disease mitigation strategies.
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Affiliation(s)
- Anshu Ankolekar
- Department of Precision Medicine, GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Lisanne Eppings
- Department of Precision Medicine, GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | | | | | | | | | - Yang Nan
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Xiaodan Xing
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Simon LF Walsh
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Wim Vos
- Radiomics (Oncoradiomics SA), Liege, Belgium
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Bioengineering Department and I-X, Imperial College London, London, United Kingdom
| | - Philippe Lambin
- Department of Precision Medicine, GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, the Netherlands
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Tomassetti S, Ciani L, Luzzi V, Gori L, Trigiani M, Giuntoli L, Lavorini F, Poletti V, Ravaglia C, Torrego A, Maldonado F, Lentz R, Annunziato F, Maggi L, Rossolini GM, Pollini S, Para O, Ciurleo G, Casini A, Rasero L, Bartoloni A, Spinicci M, Munavvar M, Gasparini S, Comin C, Cerinic MM, Peired A, Henket M, Ernst B, Louis R, Corhay JL, Nardi C, Guiot J. Utility of bronchoalveolar lavage for COVID-19: a perspective from the Dragon consortium. Front Med (Lausanne) 2024; 11:1259570. [PMID: 38371516 PMCID: PMC10869531 DOI: 10.3389/fmed.2024.1259570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 01/09/2024] [Indexed: 02/20/2024] Open
Abstract
Diagnosing COVID-19 and treating its complications remains a challenge. This review reflects the perspective of some of the Dragon (IMI 2-call 21, #101005122) research consortium collaborators on the utility of bronchoalveolar lavage (BAL) in COVID-19. BAL has been proposed as a potentially useful diagnostic tool to increase COVID-19 diagnosis sensitivity. In both critically ill and non-critically ill COVID-19 patients, BAL has a relevant role in detecting other infections or supporting alternative diagnoses and can change management decisions in up to two-thirds of patients. BAL is used to guide steroid and immunosuppressive treatment and to narrow or discontinue antibiotic treatment, reducing the use of unnecessary broad antibiotics. Moreover, cellular analysis and novel multi-omics techniques on BAL are of critical importance for understanding the microenvironment and interaction between epithelial cells and immunity, revealing novel potential prognostic and therapeutic targets. The BAL technique has been described as safe for both patients and healthcare workers in more than a thousand procedures reported to date in the literature. Based on these preliminary studies, we recognize that BAL is a feasible procedure in COVID-19 known or suspected cases, useful to properly guide patient management, and has great potential for research.
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Affiliation(s)
- Sara Tomassetti
- Interventional Pulmonology Unit, Department of Experimental and Clinical Medicine, Careggi University Hospital, Florence, Italy
| | - Luca Ciani
- Interventional Pulmonology Unit, Department of Experimental and Clinical Medicine, Careggi University Hospital, Florence, Italy
| | - Valentina Luzzi
- Interventional Pulmonology Unit, Department of Experimental and Clinical Medicine, Careggi University Hospital, Florence, Italy
| | - Leonardo Gori
- Pulmonology Unit, Department of Experimental and Clinical Medicine, Careggi University Hospital, Florence, Italy
| | - Marco Trigiani
- Interventional Pulmonology Unit, Department of Experimental and Clinical Medicine, Careggi University Hospital, Florence, Italy
| | - Leonardo Giuntoli
- Interventional Pulmonology Unit, Department of Experimental and Clinical Medicine, Careggi University Hospital, Florence, Italy
| | - Federico Lavorini
- Pulmonology Unit, Department of Experimental and Clinical Medicine, Careggi University Hospital, Florence, Italy
| | - Venerino Poletti
- Department of Diseases of the Thorax, GB Morgagni Hospital, Forlì, Italy
| | - Claudia Ravaglia
- Department of Diseases of the Thorax, GB Morgagni Hospital, Forlì, Italy
| | - Alfons Torrego
- Respiratory Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Fabien Maldonado
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Robert Lentz
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Francesco Annunziato
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Laura Maggi
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Gian Maria Rossolini
- Department of Experimental Medicine, University of Florence, Florence, Italy
- Microbiology and Virology Unit, Florence Careggi University Hospital, Florence, Italy
| | - Simona Pollini
- Department of Experimental Medicine, University of Florence, Florence, Italy
- Microbiology and Virology Unit, Florence Careggi University Hospital, Florence, Italy
| | - Ombretta Para
- Internal Medicine Unit 1, AOU Careggi, Florence, Italy
| | - Greta Ciurleo
- Internal Medicine Unit 2, AOU Careggi, Florence, Italy
| | | | - Laura Rasero
- Department of Health Science, Clinical Innovations and Research Unit, Careggi University Hospital, Florence, Italy
| | - Alessandro Bartoloni
- Infectious and Tropical Diseases Unit, Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Michele Spinicci
- Infectious and Tropical Diseases Unit, Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Mohammed Munavvar
- School of Biological Sciences, The University of Manchester, Manchester, United Kingdom
- Department of Respiratory, Lancashire Teaching Hospital NHS Foundation Trust, Preston, United Kingdom
| | - Stefano Gasparini
- Interventional Pulmonology Unit, University Hospital Riuniti di Ancona, Ancona, Italy
| | - Camilla Comin
- Department of Experimental and Clinical Medicine Section of Surgery, Histopathology, and Molecular Pathology, University of Florence, Florence, Italy
| | - Marco Matucci Cerinic
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Anna Peired
- Department of Clinical and Experimental Biomedical Sciences, University of Florence, Florence, Italy
| | - Monique Henket
- Department of Respiratory Medicine, Universitary Hospital of Liège, Liège, Belgium
| | - Benoit Ernst
- Department of Respiratory Medicine, Universitary Hospital of Liège, Liège, Belgium
| | - Renaud Louis
- Department of Respiratory Medicine, Universitary Hospital of Liège, Liège, Belgium
| | - Jean-louis Corhay
- Department of Respiratory Medicine, Universitary Hospital of Liège, Liège, Belgium
| | - Cosimo Nardi
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence, Florence, Italy
| | - Julien Guiot
- Department of Respiratory Medicine, Universitary Hospital of Liège, Liège, Belgium
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Guiot J, Walsh SLF. The ERS PROFILE.net Clinical Research Collaboration is dedicated to the set-up of an academic network to enhance imaging-based management of progressive pulmonary fibrosis. Eur Respir J 2023; 62:2300577. [PMID: 37690785 DOI: 10.1183/13993003.00577-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 07/05/2023] [Indexed: 09/12/2023]
Affiliation(s)
- Julien Guiot
- Respiratory Medicine Department, University Hospital of Liège, Liège, Belgium
| | - Simon L F Walsh
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton Hospital, London, UK
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Machado MAD, Silva RRE, Namias M, Lessa AS, Neves MCLC, Silva CTA, Oliveira DM, Reina TR, Lira AAB, Almeida LM, Zanchettin C, Netto EM. Multi-center Integrating Radiomics, Structured Reports, and Machine Learning Algorithms for Assisted Classification of COVID-19 in Lung Computed Tomography. J Med Biol Eng 2023; 43:156-162. [PMID: 37077697 PMCID: PMC9990550 DOI: 10.1007/s40846-023-00781-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 02/16/2023] [Indexed: 04/21/2023]
Abstract
Purpose To evaluate the classification performance of structured report features, radiomics, and machine learning (ML) models to differentiate between Coronavirus Disease 2019 (COVID-19) and other types of pneumonia using chest computed tomography (CT) scans. Methods Sixty-four COVID-19 subjects and 64 subjects with non-COVID-19 pneumonia were selected. The data was split into two independent cohorts: one for the structured report, radiomic feature selection and model building (n = 73), and another for model validation (n = 55). Physicians performed readings with and without machine learning support. The model's sensitivity and specificity were calculated, and inter-rater reliability was assessed using Cohen's Kappa agreement coefficient. Results Physicians performed with mean sensitivity and specificity of 83.4 and 64.3%, respectively. When assisted with machine learning, the mean sensitivity and specificity increased to 87.1 and 91.1%, respectively. In addition, machine learning improved the inter-rater reliability from moderate to substantial. Conclusion Integrating structured reports and radiomics promises assisted classification of COVID-19 in CT chest scans.
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Affiliation(s)
- Marcos A. D. Machado
- Department of Radiology, Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia 40110-040 Brazil
- Radtec Serviços em Física Médica, Salvador, Bahia 40060-330 Brazil
| | - Ronnyldo R. E. Silva
- Radtec Serviços em Física Médica, Salvador, Bahia 40060-330 Brazil
- Department of Systems and Computing, Universidade Federal de Campina Grande, Campina Grande, Paraíba 58429-900 Brazil
| | - Mauro Namias
- Department of Medical Physics, Nuclear Diagnostic Center Foundation, C1417CVE Buenos Aires, Argentina
| | - Andreia S. Lessa
- Department of Radiology, Hospital Universitário Gaffrée e Guinle, Universidade do Rio de Janeiro (UNIRIO), Rio de Janeiro, 20270-004 Brazil
| | - Margarida C. L. C. Neves
- Department of Pneumology, Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia 40110-040 Brazil
| | - Carolina T. A. Silva
- Department of Radiology, Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia 40110-040 Brazil
| | - Danillo M. Oliveira
- Department of Radiology, Hospital Universitário Alcides Carneiro/ Ebserh, Universidade Federal de Campina Grande, Campina Grande, Paraíba 58400-398 Brazil
- Northeast Regional Nuclear Science Centre (CRCN-NE), Recife, Pernambuco 50840-545 Brazil
- Nuclear Energy Department, Universidade Federal de Pernambuco, Recife, Pernambuco 50740-540 Brazil
| | - Thamiris R. Reina
- Department of Radiology, Hospital Universitário da Universidade Federal de Juiz de Fora/ Ebserh, Universidade Federal de Juiz de Fora, Juiz de Fora, Minas Gerais 36038-330 Brazil
| | - Arquimedes A. B. Lira
- Department of Radiology, Hospital Universitário Alcides Carneiro/ Ebserh, Universidade Federal de Campina Grande, Campina Grande, Paraíba 58400-398 Brazil
| | - Leandro M. Almeida
- Centro de Informática, Universidade Federal de Pernambuco, Recife, Pernambuco 50720-001 Brazil
| | - Cleber Zanchettin
- Centro de Informática, Universidade Federal de Pernambuco, Recife, Pernambuco 50720-001 Brazil
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208 USA
| | - Eduardo M. Netto
- Infectious Disease Research Laboratory, Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia 40110-040 Brazil
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Developing medical imaging AI for emerging infectious diseases. Nat Commun 2022; 13:7060. [PMID: 36400764 PMCID: PMC9672573 DOI: 10.1038/s41467-022-34234-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 10/19/2022] [Indexed: 11/19/2022] Open
Abstract
Very few of the COVID-19 ML models were fit for deployment in real-world settings. In this Comment, Huang et al. discuss the main steps required to develop clinically useful models in the context of an emerging infectious disease.
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Castro MA, Reza S, Chu WT, Bradley D, Lee JH, Crozier I, Sayre PJ, Lee BY, Mani V, Friedrich TC, O’Connor DH, Finch CL, Worwa G, Feuerstein IM, Kuhn JH, Solomon J. Toward the determination of sensitive and reliable whole-lung computed tomography features for robust standard radiomics and delta-radiomics analysis in a nonhuman primate model of coronavirus disease 2019. J Med Imaging (Bellingham) 2022; 9:066003. [PMID: 36506838 PMCID: PMC9731356 DOI: 10.1117/1.jmi.9.6.066003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 11/21/2022] [Indexed: 12/13/2022] Open
Abstract
Purpose We propose a method to identify sensitive and reliable whole-lung radiomic features from computed tomography (CT) images in a nonhuman primate model of coronavirus disease 2019 (COVID-19). Criteria used for feature selection in this method may improve the performance and robustness of predictive models. Approach Fourteen crab-eating macaques were assigned to two experimental groups and exposed to either severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or a mock inoculum. High-resolution CT scans were acquired before exposure and on several post-exposure days. Lung volumes were segmented using a deep-learning methodology, and radiomic features were extracted from the original image. The reliability of each feature was assessed by the intraclass correlation coefficient (ICC) using the mock-exposed group data. The sensitivity of each feature was assessed using the virus-exposed group data by defining a factor R that estimates the excess of variation above the maximum normal variation computed in the mock-exposed group. R and ICC were used to rank features and identify non-sensitive and unstable features. Results Out of 111 radiomic features, 43% had excellent reliability ( ICC > 0.90 ), and 55% had either good ( ICC > 0.75 ) or moderate ( ICC > 0.50 ) reliability. Nineteen features were not sensitive to the radiological manifestations of SARS-CoV-2 exposure. The sensitivity of features showed patterns that suggested a correlation with the radiological manifestations. Conclusions Features were quantified and ranked based on their sensitivity and reliability. Features to be excluded to create more robust models were identified. Applicability to similar viral pneumonia studies is also possible.
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Affiliation(s)
- Marcelo A. Castro
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Frederick, Maryland, United States,Address all correspondence to Marcelo A. Castro,
| | - Syed Reza
- National Institutes of Health, Clinical Center, Radiology and Imaging Sciences, Center for Infectious Disease Imaging, Bethesda, Maryland, United States
| | - Winston T. Chu
- National Institutes of Health, Clinical Center, Radiology and Imaging Sciences, Center for Infectious Disease Imaging, Bethesda, Maryland, United States
| | - Dara Bradley
- National Institutes of Health, Clinical Center, Radiology and Imaging Sciences, Center for Infectious Disease Imaging, Bethesda, Maryland, United States
| | - Ji Hyun Lee
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Frederick, Maryland, United States
| | - Ian Crozier
- Frederick National Laboratory for Cancer Research, Clinical Monitoring Research Program Directorate, Frederick, Maryland, United States
| | - Philip J. Sayre
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Frederick, Maryland, United States
| | - Byeong Y. Lee
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Frederick, Maryland, United States
| | - Venkatesh Mani
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Frederick, Maryland, United States
| | - Thomas C. Friedrich
- University of Wisconsin–Madison, School of Veterinary Medicine, Department of Pathobiological Sciences, Madison, Wisconsin, United States
| | - David H. O’Connor
- University of Wisconsin–Madison, Department of Pathology and Laboratory Medicine, Madison, Wisconsin, United States
| | - Courtney L. Finch
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Frederick, Maryland, United States
| | - Gabriella Worwa
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Frederick, Maryland, United States
| | - Irwin M. Feuerstein
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Frederick, Maryland, United States
| | - Jens H. Kuhn
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Frederick, Maryland, United States
| | - Jeffrey Solomon
- Frederick National Laboratory for Cancer Research, Clinical Monitoring Research Program Directorate, Frederick, Maryland, United States
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Guiot J, Maes N, Winandy M, Henket M, Ernst B, Thys M, Frix AN, Morimont P, Rousseau AF, Canivet P, Louis R, Misset B, Meunier P, Charbonnier JP, Lambermont B. Automatized lung disease quantification in patients with COVID-19 as a predictive tool to assess hospitalization severity. Front Med (Lausanne) 2022; 9:930055. [PMID: 36106317 PMCID: PMC9465374 DOI: 10.3389/fmed.2022.930055] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
The pandemic of COVID-19 led to a dramatic situation in hospitals, where staff had to deal with a huge number of patients in respiratory distress. To alleviate the workload of radiologists, we implemented an artificial intelligence (AI) - based analysis named CACOVID-CT, to automatically assess disease severity on chest CT scans obtained from those patients. We retrospectively studied CT scans obtained from 476 patients admitted at the University Hospital of Liege with a COVID-19 disease. We quantified the percentage of COVID-19 affected lung area (% AA) and the CT severity score (total CT-SS). These quantitative measurements were used to investigate the overall prognosis and patient outcome: hospital length of stay (LOS), ICU admission, ICU LOS, mechanical ventilation, and in-hospital death. Both CT-SS and % AA were highly correlated with the hospital LOS, the risk of ICU admission, the risk of mechanical ventilation and the risk of in-hospital death. Thus, CAD4COVID-CT analysis proved to be a useful tool in detecting patients with higher hospitalization severity risk. It will help for management of the patients flow. The software measured the extent of lung damage with great efficiency, thus relieving the workload of radiologists.
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Affiliation(s)
- Julien Guiot
- Respiratory Department, University Hospital of Liège, Liège, Belgium
- *Correspondence: Julien Guiot,
| | - Nathalie Maes
- Biostatistics and Medico-Economic Information Department, University Hospital of Liège, Liège, Belgium
| | - Marie Winandy
- Respiratory Department, University Hospital of Liège, Liège, Belgium
| | - Monique Henket
- Respiratory Department, University Hospital of Liège, Liège, Belgium
| | - Benoit Ernst
- Respiratory Department, University Hospital of Liège, Liège, Belgium
| | - Marie Thys
- Biostatistics and Medico-Economic Information Department, University Hospital of Liège, Liège, Belgium
| | - Anne-Noelle Frix
- Respiratory Department, University Hospital of Liège, Liège, Belgium
| | - Philippe Morimont
- Intensive Care Department, University Hospital of Liège, Liège, Belgium
| | | | - Perrine Canivet
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Renaud Louis
- Respiratory Department, University Hospital of Liège, Liège, Belgium
| | - Benoît Misset
- Intensive Care Department, University Hospital of Liège, Liège, Belgium
| | - Paul Meunier
- Department of Radiology, University Hospital of Liège, Liège, Belgium
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COVID-19 Diagnosis on Chest Radiographs with Enhanced Deep Neural Networks. Diagnostics (Basel) 2022; 12:diagnostics12081828. [PMID: 36010179 PMCID: PMC9406472 DOI: 10.3390/diagnostics12081828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/12/2022] [Accepted: 07/20/2022] [Indexed: 11/17/2022] Open
Abstract
The COVID-19 pandemic has caused a devastating impact on the social activity, economy and politics worldwide. Techniques to diagnose COVID-19 cases by examining anomalies in chest X-ray images are urgently needed. Inspired by the success of deep learning in various tasks, this paper evaluates the performance of four deep neural networks in detecting COVID-19 patients from their chest radiographs. The deep neural networks studied include VGG16, MobileNet, ResNet50 and DenseNet201. Preliminary experiments show that all deep neural networks perform promisingly, while DenseNet201 outshines other models. Nevertheless, the sensitivity rates of the models are below expectations, which can be attributed to several factors: limited publicly available COVID-19 images, imbalanced sample size for the COVID-19 class and non-COVID-19 class, overfitting or underfitting of the deep neural networks and that the feature extraction of pre-trained models does not adapt well to the COVID-19 detection task. To address these factors, several enhancements are proposed, including data augmentation, adjusted class weights, early stopping and fine-tuning, to improve the performance. Empirical results on DenseNet201 with these enhancements demonstrate outstanding performance with an accuracy of 0.999%, precision of 0.9899%, sensitivity of 0.98%, specificity of 0.9997% and F1-score of 0.9849% on the COVID-Xray-5k dataset.
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Mehrpouyan M, Zamanian H, Mehri-Kakavand G, Pursamimi M, Shalbaf A, Ghorbani M, Abbaskhani Davanloo A. Detection of stage of lung changes in COVID-19 disease based on CT images: a radiomics approach. Phys Eng Sci Med 2022; 45:747-755. [PMID: 35796865 PMCID: PMC9261171 DOI: 10.1007/s13246-022-01140-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 05/16/2022] [Indexed: 11/22/2022]
Abstract
The aim of this study is to classify patients suspected from COVID-19 to five stages as normal, early, progressive, peak, and absorption stages using radiomics approach based on lung computed tomography images. Lung CT scans of 683 people were evaluated. A set of statistical texture features was extracted from each CT image. The people were classified using the random forest algorithm as an ensemble method based on the decision trees outputs to five stages of COVID-19 disease. Proposed method attains the highest result with an accuracy of 93.55% (96.25% in normal, 74.39% in early, 100% in progressive, 82.19% in peak, and 96% in absorption stage) compared to the other three common classifiers. Radiomics method can be used for the classification of the stage of COVID-19 disease with good accuracy to help decide the length of time required to hospitalize patients, determine the type of treatment process required for patients in each category, and reduce the cost of care and treatment for hospitalized individuals.
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Affiliation(s)
- Mohammad Mehrpouyan
- Non-Communicable Diseases Research Center, Sabzevar University of Medical Sciences, Sabzevar, Iran.,Medical Physics and Radiological Sciences Department, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Hamed Zamanian
- Biomedical Engineering and Medical Physics Department, School of Medicine, Shahid Beheshti University of Medical Sciences, 19857-17443, Tehran, Iran
| | - Ghazal Mehri-Kakavand
- Department of Medical Physics, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran
| | - Mohamad Pursamimi
- Department of Medical Physics, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran
| | - Ahmad Shalbaf
- Biomedical Engineering and Medical Physics Department, School of Medicine, Shahid Beheshti University of Medical Sciences, 19857-17443, Tehran, Iran.
| | - Mahdi Ghorbani
- Biomedical Engineering and Medical Physics Department, School of Medicine, Shahid Beheshti University of Medical Sciences, 19857-17443, Tehran, Iran.
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AI-Based Chest CT Analysis for Rapid COVID-19 Diagnosis and Prognosis: A Practical Tool to Flag High-Risk Patients and Lower Healthcare Costs. Diagnostics (Basel) 2022; 12:diagnostics12071608. [PMID: 35885513 PMCID: PMC9324628 DOI: 10.3390/diagnostics12071608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 06/27/2022] [Accepted: 06/29/2022] [Indexed: 11/17/2022] Open
Abstract
Early diagnosis of COVID-19 is required to provide the best treatment to our patients, to prevent the epidemic from spreading in the community, and to reduce costs associated with the aggravation of the disease. We developed a decision tree model to evaluate the impact of using an artificial intelligence-based chest computed tomography (CT) analysis software (icolung, icometrix) to analyze CT scans for the detection and prognosis of COVID-19 cases. The model compared routine practice where patients receiving a chest CT scan were not screened for COVID-19, with a scenario where icolung was introduced to enable COVID-19 diagnosis. The primary outcome was to evaluate the impact of icolung on the transmission of COVID-19 infection, and the secondary outcome was the in-hospital length of stay. Using EUR 20000 as a willingness-to-pay threshold, icolung is cost-effective in reducing the risk of transmission, with a low prevalence of COVID-19 infections. Concerning the hospitalization cost, icolung is cost-effective at a higher value of COVID-19 prevalence and risk of hospitalization. This model provides a framework for the evaluation of AI-based tools for the early detection of COVID-19 cases. It allows for making decisions regarding their implementation in routine practice, considering both costs and effects.
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Canivet P, Desir C, Thys M, Henket M, Frix AN, Ernst B, Walsh S, Occhipinti M, Vos W, Maes N, Canivet JL, Louis R, Meunier P, Guiot J. The Role of Imaging in the Detection of Non-COVID-19 Pathologies during the Massive Screening of the First Pandemic Wave. Diagnostics (Basel) 2022; 12:diagnostics12071567. [PMID: 35885473 PMCID: PMC9324631 DOI: 10.3390/diagnostics12071567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 06/25/2022] [Accepted: 06/26/2022] [Indexed: 11/24/2022] Open
Abstract
During the COVID-19 pandemic induced by the SARS-CoV-2, numerous chest scans were carried out in order to establish the diagnosis, quantify the extension of lesions but also identify the occurrence of potential pulmonary embolisms. In this perspective, the performed chest scans provided a varied database for a retrospective analysis of non-COVID-19 chest pathologies discovered de novo. The fortuitous discovery of de novo non-COVID-19 lesions was generally not detected by the automated systems for COVID-19 pneumonia developed in parallel during the pandemic and was thus identified on chest CT by the radiologist. The objective is to use the study of the occurrence of non-COVID-19-related chest abnormalities (known and unknown) in a large cohort of patients having suffered from confirmed COVID-19 infection and statistically correlate the clinical data and the occurrence of these abnormalities in order to assess the potential of increased early detection of lesions/alterations. This study was performed on a group of 362 COVID-19-positive patients who were prescribed a CT scan in order to diagnose and predict COVID-19-associated lung disease. Statistical analysis using mean, standard deviation (SD) or median and interquartile range (IQR), logistic regression models and linear regression models were used for data analysis. Results were considered significant at the 5% critical level (p < 0.05). These de novo non-COVID-19 thoracic lesions detected on chest CT showed a significant prevalence in cardiovascular pathologies, with calcifying atheromatous anomalies approaching nearly 35.4% in patients over 65 years of age. The detection of non-COVID-19 pathologies was mostly already known, except for suspicious nodule, thyroid goiter and the ascending thoracic aortic aneurysm. The presence of vertebral compression or signs of pulmonary fibrosis has shown a significant impact on inpatient length of stay. The characteristics of the patients in this sample, both from a demographic and a tomodensitometric point of view on non-COVID-19 pathologies, influenced the length of hospital stay as well as the risk of intra-hospital death. This retrospective study showed that the potential importance of the detection of these non-COVID-19 lesions by the radiologist was essential in the management and the intra-hospital course of the patients.
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Affiliation(s)
- Perrine Canivet
- Department of Radiology, University Hospital of Liège, 4000 Liège, Belgium; (C.D.); (P.M.)
- Correspondence:
| | - Colin Desir
- Department of Radiology, University Hospital of Liège, 4000 Liège, Belgium; (C.D.); (P.M.)
| | - Marie Thys
- Department of Medico-Economic Information, University Hospital of Liège, 4000 Liège, Belgium;
| | - Monique Henket
- Department of Pneumology, University Hospital of Liège, 4000 Liège, Belgium; (M.H.); (A.-N.F.); (B.E.); (R.L.); (J.G.)
| | - Anne-Noëlle Frix
- Department of Pneumology, University Hospital of Liège, 4000 Liège, Belgium; (M.H.); (A.-N.F.); (B.E.); (R.L.); (J.G.)
| | - Benoit Ernst
- Department of Pneumology, University Hospital of Liège, 4000 Liège, Belgium; (M.H.); (A.-N.F.); (B.E.); (R.L.); (J.G.)
| | - Sean Walsh
- Radiomics (Oncoradiomics SA), 4000 Liège, Belgium; (S.W.); (M.O.); (W.V.)
| | | | - Wim Vos
- Radiomics (Oncoradiomics SA), 4000 Liège, Belgium; (S.W.); (M.O.); (W.V.)
| | - Nathalie Maes
- Biostatistics and Medico-Economic Information Department, University Hospital of Liège, 4000 Liège, Belgium;
| | - Jean Luc Canivet
- Department of Intensive Unit Care, University Hospital of Liège, 4000 Liège, Belgium;
| | - Renaud Louis
- Department of Pneumology, University Hospital of Liège, 4000 Liège, Belgium; (M.H.); (A.-N.F.); (B.E.); (R.L.); (J.G.)
| | - Paul Meunier
- Department of Radiology, University Hospital of Liège, 4000 Liège, Belgium; (C.D.); (P.M.)
| | - Julien Guiot
- Department of Pneumology, University Hospital of Liège, 4000 Liège, Belgium; (M.H.); (A.-N.F.); (B.E.); (R.L.); (J.G.)
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12
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Nagaraj Y, de Jonge G, Andreychenko A, Presti G, Fink MA, Pavlov N, Quattrocchi CC, Morozov S, Veldhuis R, Oudkerk M, van Ooijen PMA. Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting. Eur Radiol 2022; 32:6384-6396. [PMID: 35362751 PMCID: PMC8973680 DOI: 10.1007/s00330-022-08730-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 02/13/2022] [Accepted: 03/08/2022] [Indexed: 11/29/2022]
Abstract
Objective To develop an automatic COVID-19 Reporting and Data System (CO-RADS)–based classification in a multi-demographic setting. Methods This multi-institutional review boards–approved retrospective study included 2720 chest CT scans (mean age, 58 years [range 18–100 years]) from Italian and Russian patients. Three board-certified radiologists from three countries assessed randomly selected subcohorts from each population and provided CO-RADS–based annotations. CT radiomic features were extracted from the selected subcohorts after preprocessing steps like lung lobe segmentation and automatic noise reduction. We compared three machine learning models, logistic regression (LR), multilayer perceptron (MLP), and random forest (RF) for the automated CO-RADS classification. Model evaluation was carried out in two scenarios, first, training on a mixed multi-demographic subcohort and testing on an independent hold-out dataset. In the second scenario, training was done on a single demography and externally validated on the other demography. Results The overall inter-observer agreement for the CO-RADS scoring between the radiologists was substantial (k = 0.80). Irrespective of the type of validation test scenario, suspected COVID-19 CT scans were identified with an accuracy of 84%. SHapley Additive exPlanations (SHAP) interpretation showed that the “wavelet_(LH)_GLCM_Imc1” feature had a positive impact on COVID prediction both with and without noise reduction. The application of noise reduction improved the overall performance between the classifiers for all types. Conclusion Using an automated model based on the COVID-19 Reporting and Data System (CO-RADS), we achieved clinically acceptable performance in a multi-demographic setting. This approach can serve as a standardized tool for automated COVID-19 assessment. Keypoints • Automatic CO-RADS scoring of large-scale multi-demographic chest CTs with mean AUC of 0.93 ± 0.04. • Validation procedure resembles TRIPOD 2b and 3 categories, enhancing the quality of experimental design to test the cross-dataset domain shift between institutions aiding clinical integration. • Identification of COVID-19 pneumonia in the presence of community-acquired pneumonia and other comorbidities with an AUC of 0.92. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-08730-6.
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Affiliation(s)
- Yeshaswini Nagaraj
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands. .,Machine Learning Lab, DASH, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
| | - Gonda de Jonge
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Anna Andreychenko
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Gabriele Presti
- Unit of Diagnostic Imaging and Interventional Radiology, Departmental Faculty of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Matthias A Fink
- Clinic for Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany.,Translational Lung Research Center Heidelberg, member of the German Center for Lung Research, Heidelberg, Germany
| | - Nikolay Pavlov
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Carlo C Quattrocchi
- Unit of Diagnostic Imaging and Interventional Radiology, Departmental Faculty of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Sergey Morozov
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Raymond Veldhuis
- Faculty of Electrical Engineering, Mathematics Computer Science (EWI), Data management Biometrics (DMB), University of Twente, Enschede, The Netherlands
| | - Matthijs Oudkerk
- Faculty of Medical Sciences, University of Groningen, Groningen, The Netherlands.,Institute for DiagNostic Accuracy Research, Groningen, The Netherlands
| | - Peter M A van Ooijen
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.,Machine Learning Lab, DASH, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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13
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Mulrenan C, Rhode K, Fischer BM. A Literature Review on the Use of Artificial Intelligence for the Diagnosis of COVID-19 on CT and Chest X-ray. Diagnostics (Basel) 2022; 12:diagnostics12040869. [PMID: 35453917 PMCID: PMC9025113 DOI: 10.3390/diagnostics12040869] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/24/2022] [Accepted: 03/29/2022] [Indexed: 11/16/2022] Open
Abstract
A COVID-19 diagnosis is primarily determined by RT-PCR or rapid lateral-flow testing, although chest imaging has been shown to detect manifestations of the virus. This article reviews the role of imaging (CT and X-ray), in the diagnosis of COVID-19, focusing on the published studies that have applied artificial intelligence with the purpose of detecting COVID-19 or reaching a differential diagnosis between various respiratory infections. In this study, ArXiv, MedRxiv, PubMed, and Google Scholar were searched for studies using the criteria terms ‘deep learning’, ‘artificial intelligence’, ‘medical imaging’, ‘COVID-19’ and ‘SARS-CoV-2’. The identified studies were assessed using a modified version of the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). Twenty studies fulfilled the inclusion criteria for this review. Out of those selected, 11 papers evaluated the use of artificial intelligence (AI) for chest X-ray and 12 for CT. The size of datasets ranged from 239 to 19,250 images, with sensitivities, specificities and AUCs ranging from 0.789–1.00, 0.843–1.00 and 0.850–1.00. While AI demonstrates excellent diagnostic potential, broader application of this method is hindered by the lack of relevant comparators in studies, sufficiently sized datasets, and independent testing.
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Affiliation(s)
- Ciara Mulrenan
- School of Biomedical Engineering & Imaging Sciences, Kings College London, London WC2R 2LS, UK; (C.M.); (K.R.)
| | - Kawal Rhode
- School of Biomedical Engineering & Imaging Sciences, Kings College London, London WC2R 2LS, UK; (C.M.); (K.R.)
| | - Barbara Malene Fischer
- School of Biomedical Engineering & Imaging Sciences, Kings College London, London WC2R 2LS, UK; (C.M.); (K.R.)
- Rigshospitalet, Department of Clinical Physiology and Nuclear Medicine, Blegdamsvej 9, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark
- Correspondence:
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14
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Xu F, Lou K, Chen C, Chen Q, Wang D, Wu J, Zhu W, Tan W, Zhou Y, Liu Y, Wang B, Zhang X, Zhang Z, Zhang J, Sun M, Zhang G, Dai G, Hu H. An original deep learning model using limited data for COVID-19 discrimination: A multi-center study. Med Phys 2022; 49:3874-3885. [PMID: 35305027 PMCID: PMC9088453 DOI: 10.1002/mp.15549] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 12/24/2021] [Accepted: 02/07/2022] [Indexed: 11/06/2022] Open
Abstract
OBJECTIVES Artificial intelligence (AI) has been proved to be a highly efficient tool for COVID-19 diagnosis, but the large data size and heavy label force required for algorithm development and the poor generalizability of AI algorithms, to some extent, limit the application of AI technology in clinical practice. The aim of this study is to develop an AI algorithm with high robustness using limited chest CT data for COVID-19 discrimination. METHODS A three dimensional algorithm that combined multi-instance learning (MIL) with the long and short-term memory (LSTM) architecture (3DMTM) was developed for differentiating COVID-19 from community acquired pneumonia (CAP) while logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM) and a three dimensional convolutional neural network (3D CNN) set for comparison. Totally, 515 patients with or without COVID-19 between December 2019 and March 2020 from 5 different hospitals were recruited and divided into relatively large (150 COVID-19 and 183 CAP cases) and relatively small datasets (17 COVID-19 and 35 CAP cases) for either training or validation and another independent dataset (37 COVID-19 and 93 CAP cases) for external test. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, F1 score, and G-Mean were utilized for performance evaluation. RESULTS In the external test cohort, the relatively large data-based 3DMTM-LD achieved an AUC of 0.956 (95%CI, 0.929∼0.982) with 86.2% and 98.0% for its sensitivity and specificity. 3DMTM-SD got an AUC of 0.937 (95%CI, 0.909∼0.965) while the AUC of 3DCM-SD decreased dramatically to 0.714 (95%CI, 0.649∼0.780) with training data reduction. KNN-MMSD, LR-MMSD, SVM-MMSD and 3DCM-MMSD benefited significantly from the inclusion of clinical information while models trained with relatively large dataset got slight performance improvement in COVID-19 discrimination. 3DMTM, trained with either CT or multi-modal data, presented comparably excellent performance in COVID-19 discrimination. CONCLUSIONS The 3DMTM algorithm presented excellent robustness for COVID-19 discrimination with limited CT data. 3DMTM based on CT data performed comparably in COVID-19 discrimination with that trained with multi-modal information. Clinical information could improve the performance of KNN, LR, SVM and 3DCM in COVID-19 discrimination, especially in the scenario with limited data for training. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Fangyi Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No.3, Qingchun East Road, Shangcheng District, Hangzhou, Zhejiang, China
| | - Kaihua Lou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No.3, Qingchun East Road, Shangcheng District, Hangzhou, Zhejiang, China
| | - Chao Chen
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No.3, Qingchun East Road, Shangcheng District, Hangzhou, Zhejiang, China
| | - Qingqing Chen
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No.3, Qingchun East Road, Shangcheng District, Hangzhou, Zhejiang, China
| | - Dawei Wang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd., 18F, Building E. Yuanyang International Center, Chaoyang District, Beijing, China
| | - Jiangfen Wu
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd., 18F, Building E. Yuanyang International Center, Chaoyang District, Beijing, China
| | - Wenchao Zhu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No.3, Qingchun East Road, Shangcheng District, Hangzhou, Zhejiang, China
| | - Weixiong Tan
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd., 18F, Building E. Yuanyang International Center, Chaoyang District, Beijing, China
| | - Yong Zhou
- Department of Pulmonary and Critical Care Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No.3, Qingchun East Road, Shangcheng District, Hangzhou, Zhejiang, China.,China National Respiratory Regional Medical Center (East China), No.3, Qingchun East Road, Shangcheng District, Hangzhou, Zhejiang, China
| | - Yongjiu Liu
- Department of Radiology, JINGMEN NO.1 PEOPLE'S HOSPITAL, No.168, Xiangshan Road, Dongbao District, Jingmen, Hubei, China
| | - Bing Wang
- Department of Radiology, JINGMEN NO.1 PEOPLE'S HOSPITAL, No.168, Xiangshan Road, Dongbao District, Jingmen, Hubei, China
| | - Xiaoguo Zhang
- Department of respiratory medicine, Jinan Infectious Disease Hospital, Shandong University, No.22029, Jingshi Road, Shizhong District, Jinan, China
| | - Zhongfa Zhang
- Department of respiratory medicine, Jinan Infectious Disease Hospital, Shandong University, No.22029, Jingshi Road, Shizhong District, Jinan, China
| | - Jianjun Zhang
- Department of Radiology, Zhejiang Hospital, No.12, Lingyin Road, Xihu District, Hangzhou, China
| | - Mingxia Sun
- Department of Radiology, Zhejiang Hospital, No.12, Lingyin Road, Xihu District, Hangzhou, China
| | - Guohua Zhang
- Department of Radiology, TAIZHOU NO.1 PEOPLE'S HOSPITAL, No.218, Hengjie Road, Huangyan District, Taizhou, Zhejiang, China
| | - Guojiao Dai
- Department of Radiology, TAIZHOU NO.1 PEOPLE'S HOSPITAL, No.218, Hengjie Road, Huangyan District, Taizhou, Zhejiang, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No.3, Qingchun East Road, Shangcheng District, Hangzhou, Zhejiang, China
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15
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Mondal MRH, Bharati S, Podder P. CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images. PLoS One 2021; 16:e0259179. [PMID: 34710175 PMCID: PMC8553063 DOI: 10.1371/journal.pone.0259179] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 10/15/2021] [Indexed: 02/05/2023] Open
Abstract
This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.
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Affiliation(s)
- M. Rubaiyat Hossain Mondal
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Subrato Bharati
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Prajoy Podder
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
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16
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Yang N, Liu F, Li C, Xiao W, Xie S, Yuan S, Zuo W, Ma X, Jiang G. Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images. Sci Rep 2021; 11:17885. [PMID: 34504246 PMCID: PMC8429652 DOI: 10.1038/s41598-021-97497-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 08/06/2021] [Indexed: 12/21/2022] Open
Abstract
We propose a classification method using the radiomics features of CT chest images to identify patients with coronavirus disease 2019 (COVID-19) and other pneumonias. The chest CT images of two groups of participants (90 COVID-19 patients who were confirmed as positive by nucleic acid test of RT-PCR and 90 other pneumonias patients) were collected, and the two groups of data were manually drawn to outline the region of interest (ROI) of pneumonias. The radiomics method was used to extract textural features and histogram features of the ROI and obtain a radiomics features vector from each sample. Then, we divided the data into two independent radiomic cohorts for training (70 COVID-19 patients and 70 other pneumonias patients), and validation (20 COVID-19 patients and 20 other pneumonias patients) by using support vector machine (SVM). This model used 20 rounds of tenfold cross-validation for training. Finally, single-shot testing of the final model was performed on the independent validation cohort. In the COVID-19 patients, correlation analysis (multiple comparison correction-Bonferroni correction, P < 0.05/7) was also conducted to determine whether the textural and histogram features were correlated with the laboratory test index of blood, i.e., blood oxygen, white blood cell, lymphocytes, neutrophils, C-reactive protein, hypersensitive C-reactive protein, and erythrocyte sedimentation rate. The final model showed good discrimination on the independent validation cohort, with an accuracy of 89.83%, sensitivity of 94.22%, specificity of 85.44%, and AUC of 0.940. This proved that the radiomics features were highly distinguishable, and this SVM model can effectively identify and diagnose patients with COVID-19 and other pneumonias. The correlation analysis results showed that some textural features were positively correlated with WBC, and NE, and also negatively related to SPO2H and NE. Our results showed that radiomic features can classify COVID-19 patients and other pneumonias patients. The SVM model can achieve an excellent diagnosis of COVID-19.
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Affiliation(s)
- Ning Yang
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, 510317, People's Republic of China
| | - Faming Liu
- Radiology Department, Xiao Chang First People's Hospital, Hubei, People's Republic of China
| | - Chunlong Li
- Majoring in Imaging and Nuclear Medicine, Graduate School, Guangdong Medical University, Guangzhou, People's Republic of China
| | - Wenqing Xiao
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, 510317, People's Republic of China
| | - Shuangcong Xie
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, 510317, People's Republic of China
| | - Shuyi Yuan
- Equipment Department, Guangdong Second Provincial General Hospital, Guangzhou, People's Republic of China
| | - Wei Zuo
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, 510317, People's Republic of China
| | - Xiaofen Ma
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, 510317, People's Republic of China.
| | - Guihua Jiang
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, 510317, People's Republic of China.
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17
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Hao R, Zhang L, Liu J, Liu Y, Yi J, Liu X. A Promising Approach: Artificial Intelligence Applied to Small Intestinal Bacterial Overgrowth (SIBO) Diagnosis Using Cluster Analysis. Diagnostics (Basel) 2021; 11:1445. [PMID: 34441379 PMCID: PMC8392862 DOI: 10.3390/diagnostics11081445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 07/16/2021] [Accepted: 08/07/2021] [Indexed: 11/16/2022] Open
Abstract
Small intestinal bacterial overgrowth (SIBO) is characterized by abnormal and excessive amounts of bacteria in the small intestine. Since symptoms and lab tests are non-specific, the diagnosis of SIBO is highly dependent on breath testing. There is a lack of a universally accepted cut-off point for breath testing to diagnose SIBO, and the dilemma of defining "SIBO patients" has made it more difficult to explore the gold standard for SIBO diagnosis. How to validate the gold standard for breath testing without defining "SIBO patients" has become an imperious demand in clinic. Breath-testing datasets from 1071 patients were collected from Xiangya Hospital in the past 3 years and analyzed with an artificial intelligence method using cluster analysis. K-means and DBSCAN algorithms were applied to the dataset after the clustering tendency was confirmed with Hopkins Statistic. Satisfying the clustering effect was evaluated with a Silhouette score, and patterns of each group were described. Advantages of artificial intelligence application in adaptive breath-testing diagnosis criteria with SIBO were discussed from the aspects of high dimensional analysis, and data-driven and regional specific dietary influence. This research work implied a promising application of artificial intelligence for SIBO diagnosis, which would benefit clinical practice and scientific research.
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Affiliation(s)
- Rong Hao
- Department of Gastroenterology, Xiangya Hospital, Central South University, Changsha 410008, China; (R.H.); (J.L.); (Y.L.)
- Hunan International Scientific and Technological Cooperation Base of Artificial Intelligence Computer Aided Diagnosis and Treatment for Digestive Disease, Changsha 410008, China
| | - Lun Zhang
- Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology, Changsha 410072, China;
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410072, China
| | - Jiashuang Liu
- Department of Gastroenterology, Xiangya Hospital, Central South University, Changsha 410008, China; (R.H.); (J.L.); (Y.L.)
- Hunan International Scientific and Technological Cooperation Base of Artificial Intelligence Computer Aided Diagnosis and Treatment for Digestive Disease, Changsha 410008, China
| | - Yajun Liu
- Department of Gastroenterology, Xiangya Hospital, Central South University, Changsha 410008, China; (R.H.); (J.L.); (Y.L.)
- Hunan International Scientific and Technological Cooperation Base of Artificial Intelligence Computer Aided Diagnosis and Treatment for Digestive Disease, Changsha 410008, China
| | - Jun Yi
- Department of Gastroenterology, Xiangya Hospital, Central South University, Changsha 410008, China; (R.H.); (J.L.); (Y.L.)
- Hunan International Scientific and Technological Cooperation Base of Artificial Intelligence Computer Aided Diagnosis and Treatment for Digestive Disease, Changsha 410008, China
| | - Xiaowei Liu
- Department of Gastroenterology, Xiangya Hospital, Central South University, Changsha 410008, China; (R.H.); (J.L.); (Y.L.)
- Hunan International Scientific and Technological Cooperation Base of Artificial Intelligence Computer Aided Diagnosis and Treatment for Digestive Disease, Changsha 410008, China
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18
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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] [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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19
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Darcis G, Bouquegneau A, Maes N, Thys M, Henket M, Labye F, Rousseau AF, Canivet P, Desir C, Calmes D, Schils R, De Worm S, Léonard P, Meunier P, Moutschen M, Louis R, Guiot J. Long-term clinical follow-up of patients suffering from moderate-to-severe COVID-19 infection: a monocentric prospective observational cohort study. Int J Infect Dis 2021; 109:209-216. [PMID: 34273510 PMCID: PMC8278829 DOI: 10.1016/j.ijid.2021.07.016] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 07/06/2021] [Accepted: 07/06/2021] [Indexed: 12/12/2022] Open
Abstract
Objectives Various symptoms and considerable organ dysfunction persist following infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Uncertainty remains about the potential mid- and long-term health sequelae. This prospective study of patients hospitalized with coronavirus disease 2019 (COVID-19) in Liège University Hospital, Belgium aimed to determine the persistent consequences of COVID-19. Methods Patients admitted to the University Hospital of Liège with moderate-to-severe confirmed COVID-19, discharged between 2 March and 1 October 2020, were recruited prospectively. Follow-up at 3 and 6 months after hospital discharge included demographic and clinical data, biological data, pulmonary function tests (PFTs) and high-resolution computed tomography (CT) scans of the chest. Results In total, 199 individuals were included in the analysis. Most patients received oxygen supplementation (80.4%). Six months after discharge, 47% and 32% of patients still had exertional dyspnoea and fatigue. PFTs at 3-month follow-up revealed a reduced diffusion capacity of carbon monoxide (mean 71.6 ± 18.6%), and this increased significantly at 6-month follow-up (P<0.0001). Chest CT scans showed a high prevalence (68.9% of the cohort) of persistent abnormalities, mainly ground glass opacities. Duration of hospitalization, intensive care unit admission and mechanical ventilation were not associated with the persistence of symptoms 3 months after discharge. Conclusion The prevalence of persistent symptoms following hospitalization with COVID-19 is high and stable for up to 6 months after discharge. However, biological, functional and iconographic abnormalities improved significantly over time.
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Affiliation(s)
- Gilles Darcis
- Department of Infectious Diseases, University Hospital of Liège, Liège, Belgium.
| | - Antoine Bouquegneau
- Department of Nephrology-Dialysis-Transplantation, University Hospital of Liège, Liège, Belgium
| | - Nathalie Maes
- Department of Biostatistics and Medico-Economic Information, University Hospital of Liège, Liège, Belgium
| | - Marie Thys
- Department of Biostatistics and Medico-Economic Information, University Hospital of Liège, Liège, Belgium
| | - Monique Henket
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Florence Labye
- Department of Internal Medicine, University Hospital of Liège, Liège, Belgium
| | | | - Perrine Canivet
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Colin Desir
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Doriane Calmes
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Raphael Schils
- Department of Infectious Diseases, University Hospital of Liège, Liège, Belgium
| | - Sophie De Worm
- Department of Infectious Diseases, University Hospital of Liège, Liège, Belgium
| | - Philippe Léonard
- Department of Infectious Diseases, University Hospital of Liège, Liège, Belgium
| | - Paul Meunier
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Michel Moutschen
- Department of Infectious Diseases, University Hospital of Liège, Liège, Belgium
| | - Renaud Louis
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Julien Guiot
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
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20
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Lorkowski J, Kolaszyńska O, Pokorski M. Artificial Intelligence and Precision Medicine: A Perspective. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1375:1-11. [PMID: 34138457 DOI: 10.1007/5584_2021_652] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
This article aims to present how the advanced solutions of artificial intelligence and precision medicine work together to refine medical management. Multi-omics seems the most suitable approach for biological analysis of data on precision medicine and artificial intelligence. We searched PubMed and Google Scholar databases to collect pertinent articles appearing up to 5 March 2021. Genetics, oncology, radiology, and the recent coronavirus disease (COVID-19) pandemic were chosen as representative fields addressing the cross-compliance of artificial intelligence (AI) and precision medicine based on the highest number of articles, topicality, and interconnectedness of the issue. Overall, we identified and perused 1572 articles. AI is a breakthrough that takes part in shaping the Fourth Industrial Revolution in medicine and health care, changing the long-time accepted diagnostic and treatment regimens and approaches. AI-based link prediction models may be outstandingly helpful in the literature search for drug repurposing or finding new therapeutical modalities in rapidly erupting wide-scale diseases such as the recent COVID-19.
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Affiliation(s)
- Jacek Lorkowski
- Department of Orthopedics, Traumatology and Sports Medicine, Central Clinical Hospital of the Ministry of Internal Affairs and Administration, Warsaw, Poland. .,Faculty of Health Sciences, Medical University of Mazovia, Warsaw, Poland.
| | - Oliwia Kolaszyńska
- Department of Cardiology, Independent Public Regional Hospital, Szczecin, Poland
| | - Mieczysław Pokorski
- Institute of Health Sciences, Opole University, Opole, Poland.,Faculty of Health Sciences, The Jan Długosz University in Częstochowa, Częstochowa, Poland
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21
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Shiri I, Sorouri M, Geramifar P, Nazari M, Abdollahi M, Salimi Y, Khosravi B, Askari D, Aghaghazvini L, Hajianfar G, Kasaeian A, Abdollahi H, Arabi H, Rahmim A, Radmard AR, Zaidi H. Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients. Comput Biol Med 2021; 132:104304. [PMID: 33691201 PMCID: PMC7925235 DOI: 10.1016/j.compbiomed.2021.104304] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 02/26/2021] [Accepted: 02/27/2021] [Indexed: 12/16/2022]
Abstract
OBJECTIVE To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images. METHODS Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients' history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets. RESULTS For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95%CI: 0.95-0.96), accuracy = 0.88 ± 0.046 (95% CI: 0.88-0.89), sensitivity = 0.88 ± 0.066 (95% CI = 0.87-0.9) and specificity = 0.89 ± 0.07 (95% CI = 0.87-0.9)). CONCLUSION Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Majid Sorouri
- Digestive Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mostafa Nazari
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Abdollahi
- Digestive Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Bardia Khosravi
- Digestive Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Dariush Askari
- Department of Radiology Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Leila Aghaghazvini
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Amir Kasaeian
- Digestive Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran,Hematology, Oncology and Stem Cell Transplantation Research Center, Research Institute for Oncology, Hematology and Cell Therapy, Tehran University of Medical Sciences, Tehran, Iran,Inflammation Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Sciences and Medical Physics, Kerman University of Medical Sciences, Kerman, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - 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
| | - Amir Reza Radmard
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran,Corresponding author. Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, 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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22
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Chatterjee A, Wu G, Primakov S, Oberije C, Woodruff H, Kubben P, Henry R, Aries MJH, Beudel M, Noordzij PG, Dormans T, Gritters van den Oever NC, van den Bergh JP, Wyers CE, Simsek S, Douma R, Reidinga AC, de Kruif MD, Guiot J, Frix AN, Louis R, Moutschen M, Lovinfosse P, Lambin P. Can predicting COVID-19 mortality in a European cohort using only demographic and comorbidity data surpass age-based prediction: An externally validated study. PLoS One 2021; 16:e0249920. [PMID: 33857224 PMCID: PMC8049248 DOI: 10.1371/journal.pone.0249920] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 03/26/2021] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVE To establish whether one can build a mortality prediction model for COVID-19 patients based solely on demographics and comorbidity data that outperforms age alone. Such a model could be a precursor to implementing smart lockdowns and vaccine distribution strategies. METHODS The training cohort comprised 2337 COVID-19 inpatients from nine hospitals in The Netherlands. The clinical outcome was death within 21 days of being discharged. The features were derived from electronic health records collected during admission. Three feature selection methods were used: LASSO, univariate using a novel metric, and pairwise (age being half of each pair). 478 patients from Belgium were used to test the model. All modeling attempts were compared against an age-only model. RESULTS In the training cohort, the mortality group's median age was 77 years (interquartile range = 70-83), higher than the non-mortality group (median = 65, IQR = 55-75). The incidence of former/active smokers, male gender, hypertension, diabetes, dementia, cancer, chronic obstructive pulmonary disease, chronic cardiac disease, chronic neurological disease, and chronic kidney disease was higher in the mortality group. All stated differences were statistically significant after Bonferroni correction. LASSO selected eight features, novel univariate chose five, and pairwise chose none. No model was able to surpass an age-only model in the external validation set, where age had an AUC of 0.85 and a balanced accuracy of 0.77. CONCLUSION When applied to an external validation set, we found that an age-only mortality model outperformed all modeling attempts (curated on www.covid19risk.ai) using three feature selection methods on 22 demographic and comorbid features.
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Affiliation(s)
- Avishek Chatterjee
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Guangyao Wu
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Sergey Primakov
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Cary Oberije
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Henry Woodruff
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Pieter Kubben
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Ronald Henry
- Department of Internal Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Marcel J. H. Aries
- Department of Neurology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Martijn Beudel
- Department of Neurology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Peter G. Noordzij
- Department of Anesthesiology and Intensive Care, St Antonius Hospital, Nieuwegein, The Netherlands
| | - Tom Dormans
- Department of Intensive Care, Zuyderland Medical Center, Heerlen, The Netherlands
| | | | | | - Caroline E. Wyers
- Department of Internal Medicine, VieCuri Medical Centre, Venlo, The Netherlands
| | - Suat Simsek
- Department of Internal Medicine, Northwest Clinics, Alkmaar, The Netherlands
| | - Renée Douma
- Department of Internal Medicine, Flevoziekenhuis, Almere, The Netherlands
| | - Auke C. Reidinga
- Department of Intensive Care, Martiniziekenhuis, Groningen, The Netherlands
| | - Martijn D. de Kruif
- Department of Pulmonary Medicine, Zuyderland Medical Center, Heerlen, The Netherlands
| | - Julien Guiot
- Department of Respiratory Medicine, CHU of Liège, Liège, Belgium
| | - Anne-Noelle Frix
- Department of Respiratory Medicine, CHU of Liège, Liège, Belgium
| | - Renaud Louis
- Department of Respiratory Medicine, CHU of Liège, Liège, Belgium
| | | | - Pierre Lovinfosse
- Nuclear Medicine and Oncological Imaging, Department of Medical Physics, CHU of Liège, Liège, Belgium
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, The Netherlands
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23
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Wan Y, Zhou H, Zhang X. An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis. ENTROPY (BASEL, SWITZERLAND) 2021; 23:204. [PMID: 33562309 PMCID: PMC7916048 DOI: 10.3390/e23020204] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 01/27/2021] [Accepted: 02/04/2021] [Indexed: 12/15/2022]
Abstract
The Coronavirus disease 2019 (COVID-19) has become one of the threats to the world. Computed tomography (CT) is an informative tool for the diagnosis of COVID-19 patients. Many deep learning approaches on CT images have been proposed and brought promising performance. However, due to the high complexity and non-transparency of deep models, the explanation of the diagnosis process is challenging, making it hard to evaluate whether such approaches are reliable. In this paper, we propose a visual interpretation architecture for the explanation of the deep learning models and apply the architecture in COVID-19 diagnosis. Our architecture designs a comprehensive interpretation about the deep model from different perspectives, including the training trends, diagnostic performance, learned features, feature extractors, the hidden layers, the support regions for diagnostic decision, and etc. With the interpretation architecture, researchers can make a comparison and explanation about the classification performance, gain insight into what the deep model learned from images, and obtain the supports for diagnostic decisions. Our deep model achieves the diagnostic result of 94.75%, 93.22%, 96.69%, 97.27%, and 91.88% in the criteria of accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, which are 8.30%, 4.32%, 13.33%, 10.25%, and 6.19% higher than that of the compared traditional methods. The visualized features in 2-D and 3-D spaces provide the reasons for the superiority of our deep model. Our interpretation architecture would allow researchers to understand more about how and why deep models work, and can be used as interpretation solutions for any deep learning models based on convolutional neural network. It can also help deep learning methods to take a step forward in the clinical COVID-19 diagnosis field.
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Affiliation(s)
- Yuchai Wan
- Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer Science, Beijing Technology and Business University, Beijing 100048, China; (H.Z.); (X.Z.)
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24
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Liu H, Ren H, Wu Z, Xu H, Zhang S, Li J, Hou L, Chi R, Zheng H, Chen Y, Duan S, Li H, Xie Z, Wang D. CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS. J Transl Med 2021; 19:29. [PMID: 33413480 PMCID: PMC7790050 DOI: 10.1186/s12967-020-02692-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 12/29/2020] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model. METHODS This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneumonia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists using CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). RESULTS Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model. CONCLUSIONS The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.
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Affiliation(s)
- Huanhuan Liu
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China
| | - Hua Ren
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China
| | - Zengbin Wu
- Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - He Xu
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, No. 287, Changhuai Road, Bengbu, 233004, Anhui, China
| | - Shuhai Zhang
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, No. 287, Changhuai Road, Bengbu, 233004, Anhui, China
| | - Jinning Li
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China
| | - Liang Hou
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China
| | - Runmin Chi
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China
| | - Hui Zheng
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China
| | - Yanhong Chen
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China
| | | | - Huimin Li
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China
| | - Zongyu Xie
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, No. 287, Changhuai Road, Bengbu, 233004, Anhui, China.
| | - Dengbin Wang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China.
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25
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Tayarani N MH. Applications of artificial intelligence in battling against covid-19: A literature review. CHAOS, SOLITONS, AND FRACTALS 2021; 142:110338. [PMID: 33041533 PMCID: PMC7532790 DOI: 10.1016/j.chaos.2020.110338] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 10/01/2020] [Indexed: 05/14/2023]
Abstract
Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.
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Affiliation(s)
- Mohammad-H Tayarani N
- Biocomputation Group, School of Computer Science, University of Hertfordshire, Hatfield, AL10 9AB, United Kingdom
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