1
|
Chen L, Li M, Wu Z, Liu S, Huang Y. A nomogram to predict severe COVID-19 patients with increased pulmonary lesions in early days. Front Med (Lausanne) 2024; 11:1343661. [PMID: 38737763 PMCID: PMC11082326 DOI: 10.3389/fmed.2024.1343661] [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: 11/24/2023] [Accepted: 03/25/2024] [Indexed: 05/14/2024] Open
Abstract
Objectives This study aimed to predict severe coronavirus disease 2019 (COVID-19) progression in patients with increased pneumonia lesions in the early days. A simplified nomogram was developed utilizing artificial intelligence (AI)-based quantified computed tomography (CT). Methods From 17 December 2019 to 20 February 2020, a total of 246 patients were confirmed COVID-19 infected in Jingzhou Central Hospital, Hubei Province, China. Of these patients, 93 were mildly ill and had follow-up examinations in 7 days, and 61 of them had enlarged lesions on CT scans. We collected the neutrophil-to-lymphocyte ratio (NLR) and three quantitative CT features from two examinations within 7 days. The three quantitative CT features of pneumonia lesions, including ground-glass opacity volume (GV), semi-consolidation volume (SV), and consolidation volume (CV), were automatically calculated using AI. Additionally, the variation volumes of the lesions were also computed. Finally, a nomogram was developed using a multivariable logistic regression model. To simplify the model, we classified all the lesion volumes based on quartiles and curve fitting results. Results Among the 93 patients, 61 patients showed enlarged lesions on CT within 7 days, of whom 19 (31.1%) developed any severe illness. The multivariable logistic regression model included age, NLR on the second time, an increase in lesion volume, and changes in SV and CV in 7 days. The personalized prediction nomogram demonstrated strong discrimination in the sample, with an area under curve (AUC) and the receiver operating characteristic curve (ROC) of 0.961 and a 95% confidence interval (CI) of 0.917-1.000. Decision curve analysis illustrated that a nomogram based on quantitative AI was clinically useful. Conclusion The integration of CT quantitative changes, NLR, and age in this model exhibits promising performance in predicting the progression to severe illness in COVID-19 patients with early-stage pneumonia lesions. This comprehensive approach holds the potential to assist clinical decision-making.
Collapse
Affiliation(s)
- Lina Chen
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei Province, China
| | - Min Li
- Department of Radiology, Jingzhou Hospital of Traditional Chinese Medicine, Jingzhou, Hubei Province, China
| | - Zhenghong Wu
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei Province, China
| | - Sibin Liu
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei Province, China
| | - Yuanyi Huang
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei Province, China
| |
Collapse
|
2
|
Lv C, Guo W, Yin X, Liu L, Huang X, Li S, Zhang L. Innovative applications of artificial intelligence during the COVID-19 pandemic. INFECTIOUS MEDICINE 2024; 3:100095. [PMID: 38586543 PMCID: PMC10998276 DOI: 10.1016/j.imj.2024.100095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/16/2023] [Accepted: 02/18/2024] [Indexed: 04/09/2024]
Abstract
The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of pandemic management and response. In the present review, we discuss the tremendous possibilities of AI technology in addressing the global challenges posed by the COVID-19 pandemic. First, we outline the multiple impacts of the current pandemic on public health, the economy, and society. Next, we focus on the innovative applications of advanced AI technologies in key areas such as COVID-19 prediction, detection, control, and drug discovery for treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. Furthermore, high-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. Finally, we discuss future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Though promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.
Collapse
Affiliation(s)
- Chenrui Lv
- Huazhong Agricultural University, Wuhan 430070, China
| | - Wenqiang Guo
- Huazhong Agricultural University, Wuhan 430070, China
| | - Xinyi Yin
- Huazhong Agricultural University, Wuhan 430070, China
| | - Liu Liu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research, Shanghai 200001, China
| | - Xinlei Huang
- Huazhong Agricultural University, Wuhan 430070, China
| | - Shimin Li
- Huazhong Agricultural University, Wuhan 430070, China
| | - Li Zhang
- Huazhong Agricultural University, Wuhan 430070, China
| |
Collapse
|
3
|
Yilmaz G, Sezer S, Bastug A, Singh V, Gopalan R, Aydos O, Ozturk BY, Gokcinar D, Kamen A, Gramz J, Bodur H, Akbiyik F. Concordance and generalization of an AI algorithm with real-world clinical data in the pre-omicron and omicron era. Heliyon 2024; 10:e25410. [PMID: 38356547 PMCID: PMC10864957 DOI: 10.1016/j.heliyon.2024.e25410] [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: 03/05/2023] [Revised: 01/23/2024] [Accepted: 01/25/2024] [Indexed: 02/16/2024] Open
Abstract
All viruses, including SARS-CoV-2, the virus responsible for COVID-19, continue to evolve, which can lead to new variants. The objective of this study is to assess the agreement between real-world clinical data and an algorithm that utilizes laboratory markers and age to predict the progression of disease severity in COVID-19 patients during the pre-Omicron and Omicron variant periods. The study evaluated the performance of a deep learning (DL) algorithm in predicting disease severity scores for COVID-19 patients using data from the USA, Spain, and Turkey (Ankara City Hospital (ACH) data set). The algorithm was developed and validated using pre-Omicron era data and was tested on both pre-Omicron and Omicron-era data. The predictions were compared to the actual clinical outcomes using a multidisciplinary approach. The concordance index values for all datasets ranged from 0.71 to 0.81. In the ACH cohort, a negative predictive value (NPV) of 0.78 or higher was observed for severe patients in both the pre-Omicron and Omicron eras, which is consistent with the algorithm's performance in the development cohort.
Collapse
Affiliation(s)
- Gulsen Yilmaz
- Department of Medical Biochemistry, Ankara Yıldırım Beyazıt University, Ankara, Turkey
- Department of Medical Biochemistry, Ministry of Health, Ankara Bilkent City Hospital, Ankara, Turkey
| | - Sevilay Sezer
- Department of Medical Biochemistry, Ministry of Health, Ankara Bilkent City Hospital, Ankara, Turkey
| | - Aliye Bastug
- Department of Infectious Disease and Clinical Microbiology, Health Science University of Turkey, Gulhane Medical School, Ankara City Hospital, Ankara, Turkey
| | - Vivek Singh
- Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ, USA
| | - Raj Gopalan
- Siemens Healthineers, Diagnostics, Tarrytown, NY, USA
| | - Omer Aydos
- Department of Infectious Disease and Clinical Microbiology, Ankara Bilkent City Hospital, Ankara, Turkey
| | - Busra Yuce Ozturk
- Department of Infectious Disease and Clinical Microbiology, Ankara Bilkent City Hospital, Ankara, Turkey
| | - Derya Gokcinar
- Department of Anesthesiology and Reanimation, Health Science University Turkey, Ankara Bilkent City Hospital, Ankara, Turkey
| | - Ali Kamen
- Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ, USA
| | - Jamie Gramz
- Siemens Healthineers, Diagnostics, Tarrytown, NY, USA
| | - Hurrem Bodur
- Department of Infectious Disease and Clinical Microbiology, Health Science University of Turkey, Gulhane Medical School, Ankara City Hospital, Ankara, Turkey
| | - Filiz Akbiyik
- Ankara Bilkent City Hospital Laboratory, Medical Director, Siemens Healthineers, Ankara, Turkey
| |
Collapse
|
4
|
Grenier PA, Brun AL, Mellot F. [The contribution of artificial intelligence (AI) subsequent to the processing of thoracic imaging]. Rev Mal Respir 2024; 41:110-126. [PMID: 38129269 DOI: 10.1016/j.rmr.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
The contribution of artificial intelligence (AI) to medical imaging is currently the object of widespread experimentation. The development of deep learning (DL) methods, particularly convolution neural networks (CNNs), has led to performance gains often superior to those achieved by conventional methods such as machine learning. Radiomics is an approach aimed at extracting quantitative data not accessible to the human eye from images expressing a disease. The data subsequently feed machine learning models and produce diagnostic or prognostic probabilities. As for the multiple applications of AI methods in thoracic imaging, they are undergoing evaluation. Chest radiography is a practically ideal field for the development of DL algorithms able to automatically interpret X-rays. Current algorithms can detect up to 14 different abnormalities present either in isolation or in combination. Chest CT is another area offering numerous AI applications. Various algorithms have been specifically formed and validated for the detection and characterization of pulmonary nodules and pulmonary embolism, as well as segmentation and quantitative analysis of the extent of diffuse lung diseases (emphysema, infectious pneumonias, interstitial lung disease). In addition, the analysis of medical images can be associated with clinical, biological, and functional data (multi-omics analysis), the objective being to construct predictive approaches regarding disease prognosis and response to treatment.
Collapse
Affiliation(s)
- P A Grenier
- Délégation à la recherche clinique et l'innovation, hôpital Foch, Suresnes, France.
| | - A L Brun
- Service de radiologie, hôpital Foch, Suresnes, France
| | - F Mellot
- Service de radiologie, hôpital Foch, Suresnes, France
| |
Collapse
|
5
|
Pérez-López FR, Blümel JE, Vallejo MS, Rodríguez I, Tserotas K, Salinas C, Rodrigues MA, Rey C, Ojeda E, Ñañez M, Miranda C, López M, Díaz K, Dextre M, Calle A, Bencosme A. Anxiety but not menopausal status influences the risk of long-COVID-19 syndrome in women living in Latin America. Maturitas 2024; 180:107873. [PMID: 37995422 DOI: 10.1016/j.maturitas.2023.107873] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 10/08/2023] [Accepted: 10/23/2023] [Indexed: 11/25/2023]
Abstract
OBJECTIVE To study sociodemographic and clinical factors associated with the long-COVID-19 syndrome among women living in Latin American countries using undirected and directed methods. METHOD We studied 347 patients with COVID-19 (confirmed by polymerase chain reaction) living in nine Latin American countries between May 2021 and July 2022, including 70 premenopausal, 48 perimenopausal, and 229 postmenopausal women. We compared the sociodemographic and general health information of women with (n = 164) and without (n = 183) the long-COVID-19 syndrome. They also completed the Connor-Davidson Resilience Scale, the Fear of COVID-19 Scale, the Jenkins Sleep Scale, and the Menopause Rating Scale to define the minimum set of variables for adjustment. We designed a directed acyclic graph (DAG) to identify factors related to the long-COVID-19 syndrome. Data were submitted to categorical logistic regression analyses. Results are reported as means and standard deviations or β-coefficients and 95 % confidence intervals. RESULTS Women with long-COVID-19 syndrome had a poor lifestyle, severe menopause symptoms, hypertension, insomnia, depression, anxiety, chronic diseases/conditions, risk of hospitalization, sleep disturbance, and low menopause-related quality of life compared to women without the syndrome. The DAG identified the following long-COVID-19 covariates: age, obesity, anxiety, depression, cancer, lifestyle, smoking, and menstrual status. A multivariable logistic model with these covariates indicated that anxiety is the only factor to be significantly associated with long-COVID-19 syndrome, whereas other covariates were confounding factors. There was no significant influence of menopausal status on the long-COVID-19 syndrome. CONCLUSION Among factors selected by the DAG, only anxiety was significantly associated with the long-COVID-19. There was no significant influence of the menopause status on the long-COVID-19 syndrome in the studied population.
Collapse
Affiliation(s)
- Faustino R Pérez-López
- Instituto Aragonés de Ciencias de la Salud, Facultad de Medicina, Universidad de Zaragoza, Zaragoza, Spain
| | - Juan Enrique Blümel
- Departamento de Medicina Interna Sur, Facultad de Medicina, Universidad de Chile, Santiago, Chile.
| | | | - Ignacio Rodríguez
- Departamento de Obstetricia, Ginecología y Reproducción, Hospital Universitario Dexeus, Barcelona, Spain
| | | | | | - Marcio A Rodrigues
- Department Gynecology and Obstetrics, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Claudia Rey
- Medicina Ginecológica Consultorios Médicos, Buenos Aires, Argentina
| | - Eliana Ojeda
- Departamento Académico de Medicina Humana, Universidad Andina del Cusco, Cusco, Peru
| | - Mónica Ñañez
- II Cátedra de Ginecología, Universidad Nacional de Córdoba, Córdoba, Argentina
| | - Carlos Miranda
- Hospital Central FAP-Instituto Médico Miraflores, Lima, Peru
| | - Marcela López
- Departamento Ginecología y Obstetricia Universidad de Santiago y Clínica Alemana, Santiago, Chile
| | - Karen Díaz
- Centro Ciudad Mujer, Ministerio de Salud, Asunción, Paraguay
| | - Maribel Dextre
- Ginecología Obstetricia, Clínica Internacional, Clínica Javier Prado, Lima, Peru
| | - Andrés Calle
- Centro Integral de Salud Obstétrica y Femenina-CISOF, Quito, Ecuador
| | - Ascanio Bencosme
- Ginecología Obstetricia, Hospital Metropolitano de Santiago, Santiago de los Caballeros, Dominican Republic
| |
Collapse
|
6
|
Haque SBU, Zafar A. Robust Medical Diagnosis: A Novel Two-Phase Deep Learning Framework for Adversarial Proof Disease Detection in Radiology Images. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:308-338. [PMID: 38343214 PMCID: PMC11266337 DOI: 10.1007/s10278-023-00916-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/23/2023] [Accepted: 10/08/2023] [Indexed: 03/02/2024]
Abstract
In the realm of medical diagnostics, the utilization of deep learning techniques, notably in the context of radiology images, has emerged as a transformative force. The significance of artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), lies in their capacity to rapidly and accurately diagnose diseases from radiology images. This capability has been particularly vital during the COVID-19 pandemic, where rapid and precise diagnosis played a pivotal role in managing the spread of the virus. DL models, trained on vast datasets of radiology images, have showcased remarkable proficiency in distinguishing between normal and COVID-19-affected cases, offering a ray of hope amidst the crisis. However, as with any technological advancement, vulnerabilities emerge. Deep learning-based diagnostic models, although proficient, are not immune to adversarial attacks. These attacks, characterized by carefully crafted perturbations to input data, can potentially disrupt the models' decision-making processes. In the medical context, such vulnerabilities could have dire consequences, leading to misdiagnoses and compromised patient care. To address this, we propose a two-phase defense framework that combines advanced adversarial learning and adversarial image filtering techniques. We use a modified adversarial learning algorithm to enhance the model's resilience against adversarial examples during the training phase. During the inference phase, we apply JPEG compression to mitigate perturbations that cause misclassification. We evaluate our approach on three models based on ResNet-50, VGG-16, and Inception-V3. These models perform exceptionally in classifying radiology images (X-ray and CT) of lung regions into normal, pneumonia, and COVID-19 pneumonia categories. We then assess the vulnerability of these models to three targeted adversarial attacks: fast gradient sign method (FGSM), projected gradient descent (PGD), and basic iterative method (BIM). The results show a significant drop in model performance after the attacks. However, our defense framework greatly improves the models' resistance to adversarial attacks, maintaining high accuracy on adversarial examples. Importantly, our framework ensures the reliability of the models in diagnosing COVID-19 from clean images.
Collapse
Affiliation(s)
- Sheikh Burhan Ul Haque
- Department of Computer Science, Aligarh Muslim University, Uttar Pradesh, Aligarh, 202002, India.
| | - Aasim Zafar
- Department of Computer Science, Aligarh Muslim University, Uttar Pradesh, Aligarh, 202002, India
| |
Collapse
|
7
|
Li Y, Chen D, Liu S, Lin J, Wang W, Huang J, Tan L, Liang L, Wang Z, Peng K, Li Q, Jian W, Zhang Y, Peng C, Chen H, Zhang X, Zheng J. Supervised training models with or without manual lesion delineation outperform clinicians in distinguishing pulmonary cryptococcosis from lung adenocarcinoma on chest CT. Mycoses 2024; 67:e13692. [PMID: 38214431 DOI: 10.1111/myc.13692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 12/16/2023] [Accepted: 12/22/2023] [Indexed: 01/13/2024]
Abstract
BACKGROUND The role of artificial intelligence (AI) in the discrimination between pulmonary cryptococcosis (PC) and lung adenocarcinoma (LA) warrants further research. OBJECTIVES To compare the performances of AI models with clinicians in distinguishing PC from LA on chest CT. METHODS Patients diagnosed with confirmed PC or LA were retrospectively recruited from three tertiary hospitals in Guangzhou. A deep learning framework was employed to develop two models: an undelineated supervised training (UST) model utilising original CT images, and a delineated supervised training (DST) model utilising CT images with manual lesion annotations provided by physicians. A subset of 20 cases was randomly selected from the entire dataset and reviewed by clinicians through a network questionnaire. The sensitivity, specificity and accuracy of the models and the clinicians were calculated. RESULTS A total of 395 PC cases and 249 LA cases were included in the final analysis. The internal validation results for the UST model showed a sensitivity of 85.3%, specificity of 81.0%, accuracy of 83.6% and an area under the curve (AUC) of 0.93. Similarly, the DST model exhibited a sensitivity of 88.2%, specificity of 88.1%, accuracy of 88.2% and an AUC of 0.94. The external validation of the two models yielded AUC values of 0.74 and 0.77, respectively. The average sensitivity, specificity and accuracy of 102 clinicians were determined to be 63.1%, 53.7% and 59.3%, respectively. CONCLUSIONS Both models outperformed the clinicians in distinguishing between PC and LA on chest CT, with the UST model exhibiting comparable performance to the DST model.
Collapse
Affiliation(s)
- Yun Li
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Deyan Chen
- Shenyang Neusoft Intelligent Medical Technology Research Institute Co., Ltd, Shenyang, China
| | - Shuyi Liu
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Junfeng Lin
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Wei Wang
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
- Department of Information, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jinhai Huang
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Lunfang Tan
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Lina Liang
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zhufeng Wang
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Kang Peng
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Qiasheng Li
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Wenhua Jian
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Youwen Zhang
- Department of Neurology, Gaozhou People's Hospital, Gaozhou, China
| | - Chengbao Peng
- Shenyang Neusoft Intelligent Medical Technology Research Institute Co., Ltd, Shenyang, China
| | - Huai Chen
- Department of Radiology, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xia Zhang
- Shenyang Neusoft Intelligent Medical Technology Research Institute Co., Ltd, Shenyang, China
| | - Jinping Zheng
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| |
Collapse
|
8
|
Cha MJ, Solomon JJ, Lee JE, Choi H, Chae KJ, Lee KS, Lynch DA. Chronic Lung Injury after COVID-19 Pneumonia: Clinical, Radiologic, and Histopathologic Perspectives. Radiology 2024; 310:e231643. [PMID: 38193836 PMCID: PMC10831480 DOI: 10.1148/radiol.231643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 09/06/2023] [Accepted: 09/26/2023] [Indexed: 01/10/2024]
Abstract
With the COVID-19 pandemic having lasted more than 3 years, concerns are growing about prolonged symptoms and respiratory complications in COVID-19 survivors, collectively termed post-COVID-19 condition (PCC). Up to 50% of patients have residual symptoms and physiologic impairment, particularly dyspnea and reduced diffusion capacity. Studies have also shown that 24%-54% of patients hospitalized during the 1st year of the pandemic exhibit radiologic abnormalities, such as ground-glass opacity, reticular opacity, bronchial dilatation, and air trapping, when imaged more than 1 year after infection. In patients with persistent respiratory symptoms but normal results at chest CT, dual-energy contrast-enhanced CT, xenon 129 MRI, and low-field-strength MRI were reported to show abnormal ventilation and/or perfusion, suggesting that some lung injury may not be detectable with standard CT. Histologic patterns in post-COVID-19 lung disease include fibrosis, organizing pneumonia, and vascular abnormality, indicating that different pathologic mechanisms may contribute to PCC. Therefore, a comprehensive imaging approach is necessary to evaluate and diagnose patients with persistent post-COVID-19 symptoms. This review will focus on the long-term findings of clinical and radiologic abnormalities and describe histopathologic perspectives. It also addresses advanced imaging techniques and deep learning approaches that can be applied to COVID-19 survivors. This field remains an active area of research, and further follow-up studies are warranted for a better understanding of the chronic stage of the disease and developing a multidisciplinary approach for patient management.
Collapse
Affiliation(s)
- Min Jae Cha
- From the Department of Radiology, Chung-Ang University Hospital,
Seoul, Korea (M.J.C., H.C.); Departments of Medicine (J.J.S.) and Radiology
(K.J.C., D.A.L.), National Jewish Health, 1400 Jackson St, Denver, CO 80206;
Department of Radiology, Chonnam National University Hospital, Gwangju, Republic
of Korea (J.E.L.); Department of Radiology, Research Institute of Clinical
Medicine of Jeonbuk National University, Biomedical Research Institute of
Jeonbuk National University Hospital, Jeonju, Republic of Korea (K.J.C); and
Department of Radiology, Sungkyunkwan University School of Medicine and Samsung
ChangWon Hospital, Gyeongsangnam, Republic of Korea (K.S.L.)
| | - Joshua J. Solomon
- From the Department of Radiology, Chung-Ang University Hospital,
Seoul, Korea (M.J.C., H.C.); Departments of Medicine (J.J.S.) and Radiology
(K.J.C., D.A.L.), National Jewish Health, 1400 Jackson St, Denver, CO 80206;
Department of Radiology, Chonnam National University Hospital, Gwangju, Republic
of Korea (J.E.L.); Department of Radiology, Research Institute of Clinical
Medicine of Jeonbuk National University, Biomedical Research Institute of
Jeonbuk National University Hospital, Jeonju, Republic of Korea (K.J.C); and
Department of Radiology, Sungkyunkwan University School of Medicine and Samsung
ChangWon Hospital, Gyeongsangnam, Republic of Korea (K.S.L.)
| | - Jong Eun Lee
- From the Department of Radiology, Chung-Ang University Hospital,
Seoul, Korea (M.J.C., H.C.); Departments of Medicine (J.J.S.) and Radiology
(K.J.C., D.A.L.), National Jewish Health, 1400 Jackson St, Denver, CO 80206;
Department of Radiology, Chonnam National University Hospital, Gwangju, Republic
of Korea (J.E.L.); Department of Radiology, Research Institute of Clinical
Medicine of Jeonbuk National University, Biomedical Research Institute of
Jeonbuk National University Hospital, Jeonju, Republic of Korea (K.J.C); and
Department of Radiology, Sungkyunkwan University School of Medicine and Samsung
ChangWon Hospital, Gyeongsangnam, Republic of Korea (K.S.L.)
| | - Hyewon Choi
- From the Department of Radiology, Chung-Ang University Hospital,
Seoul, Korea (M.J.C., H.C.); Departments of Medicine (J.J.S.) and Radiology
(K.J.C., D.A.L.), National Jewish Health, 1400 Jackson St, Denver, CO 80206;
Department of Radiology, Chonnam National University Hospital, Gwangju, Republic
of Korea (J.E.L.); Department of Radiology, Research Institute of Clinical
Medicine of Jeonbuk National University, Biomedical Research Institute of
Jeonbuk National University Hospital, Jeonju, Republic of Korea (K.J.C); and
Department of Radiology, Sungkyunkwan University School of Medicine and Samsung
ChangWon Hospital, Gyeongsangnam, Republic of Korea (K.S.L.)
| | - Kum Ju Chae
- From the Department of Radiology, Chung-Ang University Hospital,
Seoul, Korea (M.J.C., H.C.); Departments of Medicine (J.J.S.) and Radiology
(K.J.C., D.A.L.), National Jewish Health, 1400 Jackson St, Denver, CO 80206;
Department of Radiology, Chonnam National University Hospital, Gwangju, Republic
of Korea (J.E.L.); Department of Radiology, Research Institute of Clinical
Medicine of Jeonbuk National University, Biomedical Research Institute of
Jeonbuk National University Hospital, Jeonju, Republic of Korea (K.J.C); and
Department of Radiology, Sungkyunkwan University School of Medicine and Samsung
ChangWon Hospital, Gyeongsangnam, Republic of Korea (K.S.L.)
| | - Kyung Soo Lee
- From the Department of Radiology, Chung-Ang University Hospital,
Seoul, Korea (M.J.C., H.C.); Departments of Medicine (J.J.S.) and Radiology
(K.J.C., D.A.L.), National Jewish Health, 1400 Jackson St, Denver, CO 80206;
Department of Radiology, Chonnam National University Hospital, Gwangju, Republic
of Korea (J.E.L.); Department of Radiology, Research Institute of Clinical
Medicine of Jeonbuk National University, Biomedical Research Institute of
Jeonbuk National University Hospital, Jeonju, Republic of Korea (K.J.C); and
Department of Radiology, Sungkyunkwan University School of Medicine and Samsung
ChangWon Hospital, Gyeongsangnam, Republic of Korea (K.S.L.)
| | - David A. Lynch
- From the Department of Radiology, Chung-Ang University Hospital,
Seoul, Korea (M.J.C., H.C.); Departments of Medicine (J.J.S.) and Radiology
(K.J.C., D.A.L.), National Jewish Health, 1400 Jackson St, Denver, CO 80206;
Department of Radiology, Chonnam National University Hospital, Gwangju, Republic
of Korea (J.E.L.); Department of Radiology, Research Institute of Clinical
Medicine of Jeonbuk National University, Biomedical Research Institute of
Jeonbuk National University Hospital, Jeonju, Republic of Korea (K.J.C); and
Department of Radiology, Sungkyunkwan University School of Medicine and Samsung
ChangWon Hospital, Gyeongsangnam, Republic of Korea (K.S.L.)
| |
Collapse
|
9
|
Reina-Reina A, Barrera J, Maté A, Trujillo J, Valdivieso B, Gas ME. Developing an interpretable machine learning model for predicting COVID-19 patients deteriorating prior to intensive care unit admission using laboratory markers. Heliyon 2023; 9:e22878. [PMID: 38125502 PMCID: PMC10731083 DOI: 10.1016/j.heliyon.2023.e22878] [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: 05/27/2023] [Revised: 11/15/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023] Open
Abstract
Coronavirus disease (COVID-19) remains a significant global health challenge, prompting a transition from emergency response to comprehensive management strategies. Furthermore, the emergence of new variants of concern, such as BA.2.286, underscores the need for early detection and response to new variants, which continues to be a crucial strategy for mitigating the impact of COVID-19, especially among the vulnerable population. This study aims to anticipate patients requiring intensive care or facing elevated mortality risk throughout their COVID-19 infection while also identifying laboratory predictive markers for early diagnosis of patients. Therefore, haematological, biochemical, and demographic variables were retrospectively evaluated in 8,844 blood samples obtained from 2,935 patients before intensive care unit admission using an interpretable machine learning model. Feature selection techniques were applied using precision-recall measures to address data imbalance and evaluate the suitability of the different variables. The model was trained using stratified cross-validation with k=5 and internally validated, achieving an accuracy of 77.27%, sensitivity of 78.55%, and area under the receiver operating characteristic (AUC) of 0.85; successfully identifying patients at increased risk of severe progression. From a medical perspective, the most important features of the progression or severity of patients with COVID-19 were lactate dehydrogenase, age, red blood cell distribution standard deviation, neutrophils, and platelets, which align with findings from several prior investigations. In light of these insights, diagnostic processes can be significantly expedited through the use of laboratory tests, with a greater focus on key indicators. This strategic approach not only improves diagnostic efficiency but also extends its reach to a broader spectrum of patients. In addition, it allows healthcare professionals to take early preventive measures for those most at risk of adverse outcomes, thereby optimising patient care and prognosis.
Collapse
Affiliation(s)
- A. Reina-Reina
- Lucentia Research. Department of Software and Computing System, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690, Alicante, Spain
- Lucentia Lab, Av. Pintor Pérez Gil, 16, 03540, Alicante, Spain
| | - J.M. Barrera
- Lucentia Research. Department of Software and Computing System, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690, Alicante, Spain
- Lucentia Lab, Av. Pintor Pérez Gil, 16, 03540, Alicante, Spain
| | - A. Maté
- Lucentia Research. Department of Software and Computing System, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690, Alicante, Spain
- Lucentia Lab, Av. Pintor Pérez Gil, 16, 03540, Alicante, Spain
| | - J.C. Trujillo
- Lucentia Research. Department of Software and Computing System, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690, Alicante, Spain
- Lucentia Lab, Av. Pintor Pérez Gil, 16, 03540, Alicante, Spain
| | - B. Valdivieso
- The University and Polytechnic La Fe Hospital of Valencia, Avenida Fernando Abril Martorell, 106 Torre H 1st floor, 46026, Valencia, Spain
- The Medical Research Institute of Hospital La Fe, Avenida Fernando Abril Martorell, 106 Torre F 7th floor, 46026, Valencia, Spain
| | - María-Eugenia Gas
- The Medical Research Institute of Hospital La Fe, Avenida Fernando Abril Martorell, 106 Torre F 7th floor, 46026, Valencia, Spain
| |
Collapse
|
10
|
Plasencia-Martínez JM, Pérez-Costa R, Ballesta-Ruiz M, García-Santos JM. Performance in prognostic capacity and efficiency of the Thoracic Care Suite GE AI tool applied to chest radiography of patients with COVID-19 pneumonia. RADIOLOGIA 2023; 65:509-518. [PMID: 38049250 DOI: 10.1016/j.rxeng.2022.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 11/28/2022] [Indexed: 12/06/2023]
Abstract
OBJECTIVE Rapid progression of COVID-19 pneumonia may put patients at risk of requiring ventilatory support, such as non-invasive mechanical ventilation or endotracheal intubation. Implementing tools that detect COVID-19 pneumonia can improve the patient's healthcare. We aim to evaluate the efficacy and efficiency of the artificial intelligence (AI) tool GE Healthcare's Thoracic Care Suite (featuring Lunit INSIGHT CXR, TCS) to predict the ventilatory support need based on pneumonic progression of COVID-19 on consecutive chest X-rays. METHODS Outpatients with confirmed SARS-CoV-2 infection, with chest X-ray (CXR) findings probable or indeterminate for COVID-19 pneumonia, who required a second CXR due to unfavorableclinical course, were collected. The number of affected lung fields for the two CXRs was assessed using the AI tool. RESULTS One hundred fourteen patients (57.4±14.2 years, 65-57%-men) were retrospectively collected. Fifteen (13.2%) required ventilatory support. Progression of pneumonic extension ≥0.5 lung fields per day compared to pneumonia onset, detected using the TCS tool, increased the risk of requiring ventilatory support by 4-fold. Analyzing the AI output required 26s of radiological time. CONCLUSIONS Applying the AI tool, Thoracic Care Suite, to CXR of patients with COVID-19 pneumonia allows us to anticipate ventilatory support requirements requiring less than half a minute.
Collapse
Affiliation(s)
| | - R Pérez-Costa
- Servicio de Medicina de Urgencias, Hospital General Universitario Morales Meseguer, Murcia, Spain
| | - M Ballesta-Ruiz
- Epidemiología y Salud Pública, Consejería de Salud Regional. IMIB-Arrixaca, Universidad de Murcia, Murcia, Spain
| | - J M García-Santos
- Servicio de Radiología, Hospital General Universitario Morales Meseguer, Murcia, Spain
| |
Collapse
|
11
|
Tanaka H, Maetani T, Chubachi S, Tanabe N, Shiraishi Y, Asakura T, Namkoong H, Shimada T, Azekawa S, Otake S, Nakagawara K, Fukushima T, Watase M, Terai H, Sasaki M, Ueda S, Kato Y, Harada N, Suzuki S, Yoshida S, Tateno H, Yamada Y, Jinzaki M, Hirai T, Okada Y, Koike R, Ishii M, Hasegawa N, Kimura A, Imoto S, Miyano S, Ogawa S, Kanai T, Fukunaga K. Clinical utilization of artificial intelligence-based COVID-19 pneumonia quantification using chest computed tomography - a multicenter retrospective cohort study in Japan. Respir Res 2023; 24:241. [PMID: 37798709 PMCID: PMC10552312 DOI: 10.1186/s12931-023-02530-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 09/04/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND Computed tomography (CT) imaging and artificial intelligence (AI)-based analyses have aided in the diagnosis and prediction of the severity of COVID-19. However, the potential of AI-based CT quantification of pneumonia in assessing patients with COVID-19 has not yet been fully explored. This study aimed to investigate the potential of AI-based CT quantification of COVID-19 pneumonia to predict the critical outcomes and clinical characteristics of patients with residual lung lesions. METHODS This retrospective cohort study included 1,200 hospitalized patients with COVID-19 from four hospitals. The incidence of critical outcomes (requiring the support of high-flow oxygen or invasive mechanical ventilation or death) and complications during hospitalization (bacterial infection, renal failure, heart failure, thromboembolism, and liver dysfunction) was compared between the groups of pneumonia with high/low-percentage lung lesions, based on AI-based CT quantification. Additionally, 198 patients underwent CT scans 3 months after admission to analyze prognostic factors for residual lung lesions. RESULTS The pneumonia group with a high percentage of lung lesions (N = 400) had a higher incidence of critical outcomes and complications during hospitalization than the low percentage group (N = 800). Multivariable analysis demonstrated that AI-based CT quantification of pneumonia was independently associated with critical outcomes (adjusted odds ratio [aOR] 10.5, 95% confidence interval [CI] 5.59-19.7), as well as with oxygen requirement (aOR 6.35, 95% CI 4.60-8.76), IMV requirement (aOR 7.73, 95% CI 2.52-23.7), and mortality rate (aOR 6.46, 95% CI 1.87-22.3). Among patients with follow-up CT scans (N = 198), the multivariable analysis revealed that the pneumonia group with a high percentage of lung lesions on admission (aOR 4.74, 95% CI 2.36-9.52), older age (aOR 2.53, 95% CI 1.16-5.51), female sex (aOR 2.41, 95% CI 1.13-5.11), and medical history of hypertension (aOR 2.22, 95% CI 1.09-4.50) independently predicted persistent residual lung lesions. CONCLUSIONS AI-based CT quantification of pneumonia provides valuable information beyond qualitative evaluation by physicians, enabling the prediction of critical outcomes and residual lung lesions in patients with COVID-19.
Collapse
Affiliation(s)
- Hiromu Tanaka
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Tomoki Maetani
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Shotaro Chubachi
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
| | - Naoya Tanabe
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan.
| | - Yusuke Shiraishi
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Takanori Asakura
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
- Department of Clinical Medicine (Laboratory of Bioregulatory Medicine), Kitasato University School of Pharmacy, Tokyo, Japan
- Department of Respiratory Medicine, Kitasato University, Kitasato Institute Hospital, Tokyo, Japan
| | - Ho Namkoong
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan
| | - Takashi Shimada
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Shuhei Azekawa
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Shiro Otake
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Kensuke Nakagawara
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Takahiro Fukushima
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Mayuko Watase
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Hideki Terai
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Mamoru Sasaki
- Department of Respiratory Medicine, JCHO (Japan Community Health care Organization), Saitama Medical Center, Saitama, Japan
| | - Soichiro Ueda
- Department of Respiratory Medicine, JCHO (Japan Community Health care Organization), Saitama Medical Center, Saitama, Japan
| | - Yukari Kato
- Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Norihiro Harada
- Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Shoji Suzuki
- Department of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan
| | - Shuichi Yoshida
- Department of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan
| | - Hiroki Tateno
- Department of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan
| | - Yoshitake Yamada
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Masahiro Jinzaki
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Toyohiro Hirai
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Ryuji Koike
- Health Science Research and Development Center (HeRD), Tokyo Medical and Dental University, Tokyo, Japan
| | - Makoto Ishii
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Naoki Hasegawa
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan
| | - Akinori Kimura
- Institute of Research, Tokyo Medical and Dental University, Tokyo, Japan
| | - Seiya Imoto
- Division of Health Medical Intelligence, Human Genome Center, the Institute of Medical Science, the University of Tokyo, Tokyo, Japan
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
| | - Takanori Kanai
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Koichi Fukunaga
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| |
Collapse
|
12
|
Wang C, Liu S, Tang Y, Yang H, Liu J. Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis. J Med Internet Res 2023; 25:e46340. [PMID: 37477951 PMCID: PMC10403760 DOI: 10.2196/46340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/27/2023] [Accepted: 06/30/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND Deep learning (DL) prediction models hold great promise in the triage of COVID-19. OBJECTIVE We aimed to evaluate the diagnostic test accuracy of DL prediction models for assessing and predicting the severity of COVID-19. METHODS We searched PubMed, Scopus, LitCovid, Embase, Ovid, and the Cochrane Library for studies published from December 1, 2019, to April 30, 2022. Studies that used DL prediction models to assess or predict COVID-19 severity were included, while those without diagnostic test accuracy analysis or severity dichotomies were excluded. QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2), PROBAST (Prediction Model Risk of Bias Assessment Tool), and funnel plots were used to estimate the bias and applicability. RESULTS A total of 12 retrospective studies involving 2006 patients reported the cross-sectionally assessed value of DL on COVID-19 severity. The pooled sensitivity and area under the curve were 0.92 (95% CI 0.89-0.94; I2=0.00%) and 0.95 (95% CI 0.92-0.96), respectively. A total of 13 retrospective studies involving 3951 patients reported the longitudinal predictive value of DL for disease severity. The pooled sensitivity and area under the curve were 0.76 (95% CI 0.74-0.79; I2=0.00%) and 0.80 (95% CI 0.76-0.83), respectively. CONCLUSIONS DL prediction models can help clinicians identify potentially severe cases for early triage. However, high-quality research is lacking. TRIAL REGISTRATION PROSPERO CRD42022329252; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD 42022329252.
Collapse
Affiliation(s)
- Changyu Wang
- Department of Medical Informatics, West China Medical School, Sichuan University, Chengdu, China
- West China College of Stomatology, Sichuan University, Chengdu, China
| | - Siru Liu
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Yu Tang
- Xiangya School of Medicine, Central South University, Changsha, China
| | - Hao Yang
- Information Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jialin Liu
- Department of Medical Informatics, West China Medical School, Sichuan University, Chengdu, China
- Information Center, West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|
13
|
Baik SM, Hong KS, Park DJ. Deep learning approach for early prediction of COVID-19 mortality using chest X-ray and electronic health records. BMC Bioinformatics 2023; 24:190. [PMID: 37161395 PMCID: PMC10169101 DOI: 10.1186/s12859-023-05321-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 05/05/2023] [Indexed: 05/11/2023] Open
Abstract
BACKGROUND An artificial-intelligence (AI) model for predicting the prognosis or mortality of coronavirus disease 2019 (COVID-19) patients will allow efficient allocation of limited medical resources. We developed an early mortality prediction ensemble model for COVID-19 using AI models with initial chest X-ray and electronic health record (EHR) data. RESULTS We used convolutional neural network (CNN) models (Inception-ResNet-V2 and EfficientNet) for chest X-ray analysis and multilayer perceptron (MLP), Extreme Gradient Boosting (XGBoost), and random forest (RF) models for EHR data analysis. The Gradient-weighted Class Activation Mapping and Shapley Additive Explanations (SHAP) methods were used to determine the effects of these features on COVID-19. We developed an ensemble model (Area under the receiver operating characteristic curve of 0.8698) using a soft voting method with weight differences for CNN, XGBoost, MLP, and RF models. To resolve the data imbalance, we conducted F1-score optimization by adjusting the cutoff values to optimize the model performance (F1 score of 0.77). CONCLUSIONS Our study is meaningful in that we developed an early mortality prediction model using only the initial chest X-ray and EHR data of COVID-19 patients. Early prediction of the clinical courses of patients is helpful for not only treatment but also bed management. Our results confirmed the performance improvement of the ensemble model achieved by combining AI models. Through the SHAP method, laboratory tests that indicate the factors affecting COVID-19 mortality were discovered, highlighting the importance of these tests in managing COVID-19 patients.
Collapse
Affiliation(s)
- Seung Min Baik
- Division of Critical Care Medicine, Department of Surgery, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Korea
| | - Kyung Sook Hong
- Division of Critical Care Medicine, Department of Surgery, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul, Korea
| | - Dong Jin Park
- Department of Laboratory Medicine, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 1021, Tongil-ro, Eunpyeong-gu, Seoul, 03312, Korea.
| |
Collapse
|
14
|
de Godoy MF, Chatkin JM, Rodrigues RS, Forte GC, Marchiori E, Gavenski N, Barros RC, Hochhegger B. Artificial intelligence to predict the need for mechanical ventilation in cases of severe COVID-19. Radiol Bras 2023; 56:81-85. [PMID: 37168039 PMCID: PMC10165968 DOI: 10.1590/0100-3984.2022.0049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 07/22/2022] [Indexed: 03/12/2023] Open
Abstract
Objective To determinate the accuracy of computed tomography (CT) imaging assessed by deep neural networks for predicting the need for mechanical ventilation (MV) in patients hospitalized with severe acute respiratory syndrome due to coronavirus disease 2019 (COVID-19). Materials and Methods This was a retrospective cohort study carried out at two hospitals in Brazil. We included CT scans from patients who were hospitalized due to severe acute respiratory syndrome and had COVID-19 confirmed by reverse transcription-polymerase chain reaction (RT-PCR). The training set consisted of chest CT examinations from 823 patients with COVID-19, of whom 93 required MV during hospitalization. We developed an artificial intelligence (AI) model based on convolutional neural networks. The performance of the AI model was evaluated by calculating its accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve. Results For predicting the need for MV, the AI model had a sensitivity of 0.417 and a specificity of 0.860. The corresponding area under the ROC curve for the test set was 0.68. Conclusion The high specificity of our AI model makes it able to reliably predict which patients will and will not need invasive ventilation. That makes this approach ideal for identifying high-risk patients and predicting the minimum number of ventilators and critical care beds that will be required.
Collapse
Affiliation(s)
| | - José Miguel Chatkin
- Pontifícia Universidade Católica do Rio Grande do Sul
(PUCRS), Porto Alegre, RS, Brazil
| | | | - Gabriele Carra Forte
- Pontifícia Universidade Católica do Rio Grande do Sul
(PUCRS), Porto Alegre, RS, Brazil
| | - Edson Marchiori
- Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil
| | - Nathan Gavenski
- Pontifícia Universidade Católica do Rio Grande do Sul
(PUCRS), Porto Alegre, RS, Brazil
| | - Rodrigo Coelho Barros
- Pontifícia Universidade Católica do Rio Grande do Sul
(PUCRS), Porto Alegre, RS, Brazil
| | - Bruno Hochhegger
- Pontifícia Universidade Católica do Rio Grande do Sul
(PUCRS), Porto Alegre, RS, Brazil
| |
Collapse
|
15
|
Elmahdy M, Sebro R. Radiomics analysis in medical imaging research. J Med Radiat Sci 2023; 70:3-7. [PMID: 36762402 PMCID: PMC9977659 DOI: 10.1002/jmrs.662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 01/21/2023] [Indexed: 02/11/2023] Open
Abstract
This article discusses the current research in the field of radiomics in medical imaging with emphasis on its role in fighting coronavirus disease 2019 (COVID-19). This article covers the building of radiomic models in a simple straightforward manner, while discussing radiomic models potential to help us face this pandemic.
Collapse
Affiliation(s)
- Mahmoud Elmahdy
- Department of RadiologyMayo ClinicJacksonvilleFloridaUSA,Center for Augmented IntelligenceMayo ClinicJacksonvilleFloridaUSA
| | - Ronnie Sebro
- Department of RadiologyMayo ClinicJacksonvilleFloridaUSA,Center for Augmented IntelligenceMayo ClinicJacksonvilleFloridaUSA,Department of Orthopedic SurgeryMayo ClinicJacksonvilleFloridaUSA,Department of BiostatisticsCentre for Quantitative Health SciencesJacksonvilleFloridaUSA
| |
Collapse
|
16
|
Yin Y, Lu J, Tong J, Cheng Y, Zhang K. Relationship between early lung adenocarcinoma and multiple driving genes based on artificial intelligence medical images of pulmonary nodules. Front Genet 2023; 14:1142795. [PMID: 36896233 PMCID: PMC9988930 DOI: 10.3389/fgene.2023.1142795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Lung adenocarcinoma is one of the most common cancers in the world, and accurate diagnosis of lung nodules is an important factor in reducing its mortality. In the diagnosis of pulmonary nodules, artificial intelligence (AI) assisted diagnosis technology has been rapidly developed, so testing its effectiveness is conducive to promoting its important role in clinical practice. This paper introduces the background of early lung adenocarcinoma and lung nodule AI medical imaging, and then makes academic research on early lung adenocarcinoma and AI medical imaging, and finally summarizes the biological information. In the experimental part, the relationship analysis of 4 driver genes in group X and group Y showed that there were more abnormal invasive lung adenocarcinoma genes, and the maximum uptake value and uptake function of metabolic value were also higher. However, there was no significant correlation between mutations in the four driver genes and metabolic values, and the average accuracy of AI-based medical images was 3.88% higher than that of traditional images.
Collapse
Affiliation(s)
- Yajun Yin
- Department of Cardiothoracic Surgery, Changzhou Second People’s Hospital, The Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu Province, China
| | - Jiawei Lu
- Department of Cardiothoracic Surgery, Changzhou Second People’s Hospital, The Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu Province, China
| | - Jichun Tong
- Department of Cardiothoracic Surgery, Changzhou Second People’s Hospital, The Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu Province, China
| | - Youshuang Cheng
- Department of Cardiothoracic Surgery, Affiliated Ninth People’s Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Ke Zhang
- Department of Cardiothoracic Surgery, Changzhou Second People’s Hospital, The Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu Province, China
| |
Collapse
|
17
|
Plasencia-Martínez JM, Pérez-Costa R, Ballesta-Ruiz M, María García-Santos J. [Performance in prognostic capacity and efficiency of the Thoracic Care Suite GE AI tool applied to chest radiography of patients with COVID-19 pneumonia]. RADIOLOGIA 2023; 65:S0033-8338(23)00027-9. [PMID: 36744156 PMCID: PMC9886647 DOI: 10.1016/j.rx.2022.11.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 11/28/2022] [Indexed: 02/01/2023]
Abstract
OBJECTIVE Rapid progression of COVID-19 pneumonia may put patients at risk of requiring ventilatory support, such as non-invasive mechanical ventilation or endotracheal intubation. Implementing tools that detect COVID-19 pneumonia can improve the patient's healthcare. We aim to evaluate the efficacy and efficiency of the artificial intelligence (AI) tool GE Healthcare's Thoracic Care Suite (featuring Lunit INSIGHT CXR, TCS) to predict the ventilatory support need based on pneumonic progression of COVID-19 on consecutive chest X-rays. METHODS Outpatients with confirmed SARS-CoV-2 infection, with chest X-ray (CXR) findings probable or indeterminate for COVID-19 pneumonia, who required a second CXR due to unfavorable clinical course, were collected. The number of affected lung fields for the two CXRs was assessed using the AI tool. RESULTS One hundred fourteen patients (57.4 ± 14.2 years, 65 -57%- men) were retrospectively collected. Fifteen (13.2%) required ventilatory support. Progression of pneumonic extension ≥ 0.5 lung fields per day compared to pneumonia onset, detected using the TCS tool, increased the risk of requiring ventilatory support by 4-fold. Analyzing the AI output required 26 seconds of radiological time. CONCLUSIONS Applying the AI tool, Thoracic Care Suite, to CXR of patients with COVID-19 pneumonia allows us to anticipate ventilatory support requirements requiring less than half a minute.
Collapse
Affiliation(s)
- Juana María Plasencia-Martínez
- Hospital General Universitario Morales Meseguer, Servicio de radiología, Avenida Marqués de los Vélez, s/n, 30008 Murcia, España
| | - Rafael Pérez-Costa
- Hospital General Universitario Morales Meseguer, Servicio de medicina de urgencias, Avenida Marqués de los Vélez, s/n, 30008 Murcia, España
| | - Mónica Ballesta-Ruiz
- Epidemiología y Salud Pública, Consejería de Salud Regional. IMIB-Arrixaca, Universidad de Murcia, España
| | - José María García-Santos
- Hospital General Universitario Morales Meseguer, Servicio de radiología, Avenida Marqués de los Vélez, s/n, 30008 Murcia, España
| |
Collapse
|
18
|
Zhao Y, He B, Xu Z, Zhang Y, Zhao X, Huang ZA, Yang F, Wang L, Duan L, Song J, Yao J. Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire. Brief Bioinform 2023; 24:6960620. [PMID: 36567255 DOI: 10.1093/bib/bbac555] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/04/2022] [Accepted: 11/15/2022] [Indexed: 12/27/2022] Open
Abstract
Underlying medical conditions, such as cancer, kidney disease and heart failure, are associated with a higher risk for severe COVID-19. Accurate classification of COVID-19 patients with underlying medical conditions is critical for personalized treatment decision and prognosis estimation. In this study, we propose an interpretable artificial intelligence model termed VDJMiner to mine the underlying medical conditions and predict the prognosis of COVID-19 patients according to their immune repertoires. In a cohort of more than 1400 COVID-19 patients, VDJMiner accurately identifies multiple underlying medical conditions, including cancers, chronic kidney disease, autoimmune disease, diabetes, congestive heart failure, coronary artery disease, asthma and chronic obstructive pulmonary disease, with an average area under the receiver operating characteristic curve (AUC) of 0.961. Meanwhile, in this same cohort, VDJMiner achieves an AUC of 0.922 in predicting severe COVID-19. Moreover, VDJMiner achieves an accuracy of 0.857 in predicting the response of COVID-19 patients to tocilizumab treatment on the leave-one-out test. Additionally, VDJMiner interpretively mines and scores V(D)J gene segments of the T-cell receptors that are associated with the disease. The identified associations between single-cell V(D)J gene segments and COVID-19 are highly consistent with previous studies. The source code of VDJMiner is publicly accessible at https://github.com/TencentAILabHealthcare/VDJMiner. The web server of VDJMiner is available at https://gene.ai.tencent.com/VDJMiner/.
Collapse
Affiliation(s)
- Yu Zhao
- AI Lab, Tencent, Shenzhen, China
| | - Bing He
- AI Lab, Tencent, Shenzhen, China
| | | | - Yidan Zhang
- AI Lab, Tencent, Shenzhen, China.,School of Computer Science, Sichuan University, Chengdu, China
| | | | - Zhi-An Huang
- AI Lab, Tencent, Shenzhen, China.,Center for Computer Science and Information Technology, City University of Hong Kong Dongguan Research Institute, Dongguan, China
| | - Fan Yang
- AI Lab, Tencent, Shenzhen, China
| | | | - Lei Duan
- School of Computer Science, Sichuan University, Chengdu, China
| | - Jiangning Song
- AI Lab, Tencent, Shenzhen, China.,Monash Biomedicine Discovery Institute and Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
| | | |
Collapse
|
19
|
Walston SL, Matsumoto T, Miki Y, Ueda D. Artificial intelligence-based model for COVID-19 prognosis incorporating chest radiographs and clinical data; a retrospective model development and validation study. Br J Radiol 2022; 95:20220058. [PMID: 36193755 PMCID: PMC9733620 DOI: 10.1259/bjr.20220058] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 08/19/2022] [Accepted: 08/23/2022] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVES The purpose of this study was to develop an artificial intelligence-based model to prognosticate COVID-19 patients at admission by combining clinical data and chest radiographs. METHODS This retrospective study used the Stony Brook University COVID-19 dataset of 1384 inpatients. After exclusions, 1356 patients were randomly divided into training (1083) and test datasets (273). We implemented three artificial intelligence models, which classified mortality, ICU admission, or ventilation risk. Each model had three submodels with different inputs: clinical data, chest radiographs, and both. We showed the importance of the variables using SHapley Additive exPlanations (SHAP) values. RESULTS The mortality prediction model was best overall with area under the curve, sensitivity, specificity, and accuracy of 0.79 (0.72-0.86), 0.74 (0.68-0.79), 0.77 (0.61-0.88), and 0.74 (0.69-0.79) for the clinical data-based model; 0.77 (0.69-0.85), 0.67 (0.61-0.73), 0.81 (0.67-0.92), 0.70 (0.64-0.75) for the image-based model, and 0.86 (0.81-0.91), 0.76 (0.70-0.81), 0.77 (0.61-0.88), 0.76 (0.70-0.81) for the mixed model. The mixed model had the best performance (p value < 0.05). The radiographs ranked fourth for prognostication overall, and first of the inpatient tests assessed. CONCLUSIONS These results suggest that prognosis models become more accurate if AI-derived chest radiograph features and clinical data are used together. ADVANCES IN KNOWLEDGE This AI model evaluates chest radiographs together with clinical data in order to classify patients as having high or low mortality risk. This work shows that chest radiographs taken at admission have significant COVID-19 prognostic information compared to clinical data other than age and sex.
Collapse
Affiliation(s)
| | | | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University,1-4-3 Asahi-machi, Abeno-ku, Osaka, Japan
| | | |
Collapse
|
20
|
Hwangbo S, Kim Y, Lee C, Lee S, Oh B, Moon MK, Kim SW, Park T. Machine learning models to predict the maximum severity of COVID-19 based on initial hospitalization record. Front Public Health 2022; 10:1007205. [PMID: 36518574 PMCID: PMC9742409 DOI: 10.3389/fpubh.2022.1007205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 11/07/2022] [Indexed: 11/29/2022] Open
Abstract
Background As the worldwide spread of coronavirus disease 2019 (COVID-19) continues for a long time, early prediction of the maximum severity is required for effective treatment of each patient. Objective This study aimed to develop predictive models for the maximum severity of hospitalized COVID-19 patients using artificial intelligence (AI)/machine learning (ML) algorithms. Methods The medical records of 2,263 COVID-19 patients admitted to 10 hospitals in Daegu, Korea, from February 18, 2020, to May 19, 2020, were comprehensively reviewed. The maximum severity during hospitalization was divided into four groups according to the severity level: mild, moderate, severe, and critical. The patient's initial hospitalization records were used as predictors. The total dataset was randomly split into a training set and a testing set in a 2:1 ratio, taking into account the four maximum severity groups. Predictive models were developed using the training set and were evaluated using the testing set. Two approaches were performed: using four groups based on original severity levels groups (i.e., 4-group classification) and using two groups after regrouping the four severity level into two (i.e., binary classification). Three variable selection methods including randomForestSRC were performed. As AI/ML algorithms for 4-group classification, GUIDE and proportional odds model were used. For binary classification, we used five AI/ML algorithms, including deep neural network and GUIDE. Results Of the four maximum severity groups, the moderate group had the highest percentage (1,115 patients; 49.5%). As factors contributing to exacerbation of maximum severity, there were 25 statistically significant predictors through simple analysis of linear trends. As a result of model development, the following three models based on binary classification showed high predictive performance: (1) Mild vs. Above Moderate, (2) Below Moderate vs. Above Severe, and (3) Below Severe vs. Critical. The performance of these three binary models was evaluated using AUC values 0.883, 0.879, and, 0.887, respectively. Based on results for each of the three predictive models, we developed web-based nomograms for clinical use (http://statgen.snu.ac.kr/software/nomogramDaeguCovid/). Conclusions We successfully developed web-based nomograms predicting the maximum severity. These nomograms are expected to help plan an effective treatment for each patient in the clinical field.
Collapse
Affiliation(s)
- Suhyun Hwangbo
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
- Department of Genomic Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Yoonjung Kim
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Chanhee Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
| | - Seungyeoun Lee
- Department of Mathematics and Statistics, Sejong University, Seoul, South Korea
| | - Bumjo Oh
- Department of Family Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, South Korea
| | - Min Kyong Moon
- Department of Internal Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Shin-Woo Kim
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Taesung Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
- Department of Statistics, Seoul National University, Seoul, South Korea
| |
Collapse
|
21
|
Palmisano A, Vignale D, Boccia E, Nonis A, Gnasso C, Leone R, Montagna M, Nicoletti V, Bianchi AG, Brusamolino S, Dorizza A, Moraschini M, Veettil R, Cereda A, Toselli M, Giannini F, Loffi M, Patelli G, Monello A, Iannopollo G, Ippolito D, Mancini EM, Pontone G, Vignali L, Scarnecchia E, Iannacone M, Baffoni L, Sperandio M, de Carlini CC, Sironi S, Rapezzi C, Antiga L, Jagher V, Di Serio C, Furlanello C, Tacchetti C, Esposito A. AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients. Radiol Med 2022; 127:960-972. [PMID: 36038790 PMCID: PMC9423702 DOI: 10.1007/s11547-022-01518-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 06/13/2022] [Indexed: 02/06/2023]
Abstract
Purpose To develop and validate an effective and user-friendly AI platform based on a few unbiased clinical variables integrated with advanced CT automatic analysis for COVID-19 patients’ risk stratification. Material and Methods In total, 1575 consecutive COVID-19 adults admitted to 16 hospitals during wave 1 (February 16-April 29, 2020), submitted to chest CT within 72 h from admission, were retrospectively enrolled. In total, 107 variables were initially collected; 64 extracted from CT. The outcome was survival. A rigorous AI model selection framework was adopted for models selection and automatic CT data extraction. Model performances were compared in terms of AUC. A web–mobile interface was developed using Microsoft PowerApps environment. The platform was externally validated on 213 COVID-19 adults prospectively enrolled during wave 2 (October 14-December 31, 2020). Results The final cohort included 1125 patients (292 non-survivors, 26%) and 24 variables. Logistic showed the best performance on the complete set of variables (AUC = 0.839 ± 0.009) as in models including a limited set of 13 and 5 variables (AUC = 0.840 ± 0.0093 and AUC = 0.834 ± 0.007). For non-inferior performance, the 5 variables model (age, sex, saturation, well-aerated lung parenchyma and cardiothoracic vascular calcium) was selected as the final model and the extraction of CT-derived parameters was fully automatized. The fully automatic model showed AUC = 0.842 (95% CI: 0.816–0.867) on wave 1 and was used to build a 0–100 scale risk score (AI-SCoRE). The predictive performance was confirmed on wave 2 (AUC 0.808; 95% CI: 0.7402–0.8766). Conclusions AI-SCoRE is an effective and reliable platform for automatic risk stratification of COVID-19 patients based on a few unbiased clinical data and CT automatic analysis. Supplementary Information The online version contains supplementary material available at 10.1007/s11547-022-01518-0.
Collapse
Affiliation(s)
- Anna Palmisano
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy
| | - Davide Vignale
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy
| | - Edda Boccia
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy
| | - Alessandro Nonis
- Centro Universitario Di Statistica Per Le Scienze Biomediche, Vita-Salute San Raffaele University, Milan, Italy
| | - Chiara Gnasso
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy
| | - Riccardo Leone
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy
| | - Marco Montagna
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy
| | - Valeria Nicoletti
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy
| | | | | | | | | | | | - Alberto Cereda
- GVM Care & Research Maria Cecilia Hospital, Cotignola, Italy
| | - Marco Toselli
- GVM Care & Research Maria Cecilia Hospital, Cotignola, Italy
| | | | | | | | | | | | | | | | | | | | - Elisa Scarnecchia
- ASST Valtellina and Alto Lario, Eugenio Morelli Hospital, Sondalo, Italy
| | - Mario Iannacone
- San Giovanni Bosco Hospital, ASL Città di Torino, Turin, Italy
| | - Lucio Baffoni
- Casa di Cura Villa dei Pini, Civitanova Marche, Italy
| | | | | | | | - Claudio Rapezzi
- GVM Care & Research Maria Cecilia Hospital, Cotignola, Italy
- Cardiologic Centre, University of Ferrara, Ferrara, Italy
| | | | | | - Clelia Di Serio
- Centro Universitario Di Statistica Per Le Scienze Biomediche, Vita-Salute San Raffaele University, Milan, Italy
| | | | - Carlo Tacchetti
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy.
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy.
| | - Antonio Esposito
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy.
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy.
| |
Collapse
|
22
|
Cherrez-Ojeda I, Cortés-Telles A, Gochicoa-Rangel L, Camacho-Leon G, Mautong H, Robles-Velasco K, Faytong-Haro M. Challenges in the Management of Post-COVID-19 Pulmonary Fibrosis for the Latin American Population. J Pers Med 2022; 12:1393. [PMID: 36143178 PMCID: PMC9501763 DOI: 10.3390/jpm12091393] [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: 07/31/2022] [Revised: 08/23/2022] [Accepted: 08/24/2022] [Indexed: 11/16/2022] Open
Abstract
This commentary aims to highlight some of the major issues (with possible solutions) that the Latin American region is currently dealing with in managing post-COVID-19 pulmonary fibrosis. Overall, there is little evidence for successful long-term COVID-19 follow-up treatment. The lack of knowledge regarding proper treatment is exacerbated in Latin America by a general lack of resources devoted to healthcare, and a lack of availability and access to multidisciplinary teams. The discussion suggests that better infrastructure (primarily multicenter cohorts of COVID-19 survivors) and well-designed studies are required to develop scientific knowledge to improve treatment for the increasing prevalence of pulmonary fibrosis in Latin America.
Collapse
Affiliation(s)
- Ivan Cherrez-Ojeda
- School of Health, Universidad de Especialidades Espíritu Santo, Samborondón 0901952, Guayas, Ecuador
- Respiralab Research Group, Guayaquil 090512, Guayas, Ecuador
| | - Arturo Cortés-Telles
- Departamento de Neumología y Cirugía de Tórax, Hospital Regional de Alta Especialidad de Yucatán, Mérida 97133, Mexico
| | - Laura Gochicoa-Rangel
- Department of Respiratory Physiology, National Institute of Respiratory Diseases “Ismael Cosío Villegas”, Mexico City 14080, Mexico
| | - Génesis Camacho-Leon
- Division of Clinical and Translational Research, Larkin Community Hospital, South Miami, FL 33143, USA
| | - Hans Mautong
- School of Health, Universidad de Especialidades Espíritu Santo, Samborondón 0901952, Guayas, Ecuador
- Respiralab Research Group, Guayaquil 090512, Guayas, Ecuador
| | - Karla Robles-Velasco
- School of Health, Universidad de Especialidades Espíritu Santo, Samborondón 0901952, Guayas, Ecuador
- Respiralab Research Group, Guayaquil 090512, Guayas, Ecuador
| | - Marco Faytong-Haro
- School of Health, Universidad de Especialidades Espíritu Santo, Samborondón 0901952, Guayas, Ecuador
- Sociology and Demography Department, The Pennsylvania State University, University Park, PA 16802, USA
- Ecuadorian Development Research Lab, Daule 090656, Guayas, Ecuador
| |
Collapse
|
23
|
Chen X, Zhang Y, Cao G, Zhou J, Lin Y, Chen B, Nie K, Fu G, Su MY, Wang M. Dynamic change of COVID-19 lung infection evaluated using co-registration of serial chest CT images. Front Public Health 2022; 10:915615. [PMID: 36033815 PMCID: PMC9412202 DOI: 10.3389/fpubh.2022.915615] [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: 04/08/2022] [Accepted: 07/18/2022] [Indexed: 01/22/2023] Open
Abstract
Purpose To evaluate the volumetric change of COVID-19 lesions in the lung of patients receiving serial CT imaging for monitoring the evolution of the disease and the response to treatment. Materials and methods A total of 48 patients, 28 males and 20 females, who were confirmed to have COVID-19 infection and received chest CT examination, were identified. The age range was 21-93 years old, with a mean of 54 ± 18 years. Of them, 33 patients received the first follow-up (F/U) scan, 29 patients received the second F/U scan, and 11 patients received the third F/U scan. The lesion region of interest (ROI) was manually outlined. A two-step registration method, first using the Affine alignment, followed by the non-rigid Demons algorithm, was developed to match the lung areas on the baseline and F/U images. The baseline lesion ROI was mapped to the F/U images using the obtained geometric transformation matrix, and the radiologist outlined the lesion ROI on F/U CT again. Results The median (interquartile range) lesion volume (cm3) was 30.9 (83.1) at baseline CT exam, 18.3 (43.9) at first F/U, 7.6 (18.9) at second F/U, and 0.6 (19.1) at third F/U, which showed a significant trend of decrease with time. The two-step registration could significantly decrease the mean squared error (MSE) between baseline and F/U images with p < 0.001. The method could match the lung areas and the large vessels inside the lung. When using the mapped baseline ROIs as references, the second-look ROI drawing showed a significantly increased volume, p < 0.05, presumably due to the consideration of all the infected areas at baseline. Conclusion The results suggest that the registration method can be applied to assist in the evaluation of longitudinal changes of COVID-19 lesions on chest CT.
Collapse
Affiliation(s)
- Xiao Chen
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yang Zhang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, United States,Department of Radiological Sciences, University of California, Irvine, CA, United States
| | - Guoquan Cao
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jiahuan Zhou
- Department of Radiology, Yuyao Hospital of Traditional Chinese Medicine, Ningbo, China
| | - Ya Lin
- The People's Hospital of Cangnan, Wenzhou, China
| | | | - Ke Nie
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Gangze Fu
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China,*Correspondence: Gangze Fu
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, CA, United States,Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan,Min-Ying Su
| | - Meihao Wang
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China,Meihao Wang
| |
Collapse
|
24
|
Rahimi E, Shahisavandi M, Royo AC, Azizi M, el Bouhaddani S, Sigari N, Sturkenboom M, Ahmadizar F. The risk profile of patients with COVID-19 as predictors of lung lesions severity and mortality—Development and validation of a prediction model. Front Microbiol 2022; 13:893750. [PMID: 35958125 PMCID: PMC9361066 DOI: 10.3389/fmicb.2022.893750] [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: 03/10/2022] [Accepted: 06/30/2022] [Indexed: 11/23/2022] Open
Abstract
Objective We developed and validated a prediction model based on individuals' risk profiles to predict the severity of lung involvement and death in patients hospitalized with coronavirus disease 2019 (COVID-19) infection. Methods In this retrospective study, we studied hospitalized COVID-19 patients with data on chest CT scans performed during hospital stay (February 2020-April 2021) in a training dataset (TD) (n = 2,251) and an external validation dataset (eVD) (n = 993). We used the most relevant demographical, clinical, and laboratory variables (n = 25) as potential predictors of COVID-19-related outcomes. The primary and secondary endpoints were the severity of lung involvement quantified as mild (≤25%), moderate (26–50%), severe (>50%), and in-hospital death, respectively. We applied random forest (RF) classifier, a machine learning technique, and multivariable logistic regression analysis to study our objectives. Results In the TD and the eVD, respectively, the mean [standard deviation (SD)] age was 57.9 (18.0) and 52.4 (17.6) years; patients with severe lung involvement [n (%):185 (8.2) and 116 (11.7)] were significantly older [mean (SD) age: 64.2 (16.9), and 56.2 (18.9)] than the other two groups (mild and moderate). The mortality rate was higher in patients with severe (64.9 and 38.8%) compared to moderate (5.5 and 12.4%) and mild (2.3 and 7.1%) lung involvement. The RF analysis showed age, C reactive protein (CRP) levels, and duration of hospitalizations as the three most important predictors of lung involvement severity at the time of the first CT examination. Multivariable logistic regression analysis showed a significant strong association between the extent of the severity of lung involvement (continuous variable) and death; adjusted odds ratio (OR): 9.3; 95% CI: 7.1–12.1 in the TD and 2.6 (1.8–3.5) in the eVD. Conclusion In hospitalized patients with COVID-19, the severity of lung involvement is a strong predictor of death. Age, CRP levels, and duration of hospitalizations are the most important predictors of severe lung involvement. A simple prediction model based on available clinical and imaging data provides a validated tool that predicts the severity of lung involvement and death probability among hospitalized patients with COVID-19.
Collapse
Affiliation(s)
- Ezat Rahimi
- Clinical Research Unit, Department of Internal Medicine, Kowsar Hospital, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Mina Shahisavandi
- Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Albert Cid Royo
- Department of Datascience and Biostatistics, University Medical Center Utrecht, Utrecht, Netherlands
| | - Mohammad Azizi
- School of Medicine, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Said el Bouhaddani
- Department of Datascience and Biostatistics, University Medical Center Utrecht, Utrecht, Netherlands
| | - Naseh Sigari
- Lung Diseases and Allergy Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Miriam Sturkenboom
- Department of Datascience and Biostatistics, University Medical Center Utrecht, Utrecht, Netherlands
| | - Fariba Ahmadizar
- Department of Datascience and Biostatistics, University Medical Center Utrecht, Utrecht, Netherlands
- *Correspondence: Fariba Ahmadizar
| |
Collapse
|
25
|
Alshayeji MH, ChandraBhasi Sindhu S, Abed S. CAD systems for COVID-19 diagnosis and disease stage classification by segmentation of infected regions from CT images. BMC Bioinformatics 2022; 23:264. [PMID: 35794537 PMCID: PMC9261058 DOI: 10.1186/s12859-022-04818-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/30/2022] [Indexed: 11/19/2022] Open
Abstract
Background Here propose a computer-aided diagnosis (CAD) system to differentiate COVID-19 (the coronavirus disease of 2019) patients from normal cases, as well as to perform infection region segmentation along with infection severity estimation using computed tomography (CT) images. The developed system facilitates timely administration of appropriate treatment by identifying the disease stage without reliance on medical professionals. So far, this developed model gives the most accurate, fully automatic COVID-19 real-time CAD framework. Results The CT image dataset of COVID-19 and non-COVID-19 individuals were subjected to conventional ML stages to perform binary classification. In the feature extraction stage, SIFT, SURF, ORB image descriptors and bag of features technique were implemented for the appropriate differentiation of chest CT regions affected with COVID-19 from normal cases. This is the first work introducing this concept for COVID-19 diagnosis application. The preferred diverse database and selected features that are invariant to scale, rotation, distortion, noise etc. make this framework real-time applicable. Also, this fully automatic approach which is faster compared to existing models helps to incorporate it into CAD systems. The severity score was measured based on the infected regions along the lung field. Infected regions were segmented through a three-class semantic segmentation of the lung CT image. Using severity score, the disease stages were classified as mild if the lesion area covers less than 25% of the lung area; moderate if 25–50% and severe if greater than 50%. Our proposed model resulted in classification accuracy of 99.7% with a PNN classifier, along with area under the curve (AUC) of 0.9988, 99.6% sensitivity, 99.9% specificity and a misclassification rate of 0.0027. The developed infected region segmentation model gave 99.47% global accuracy, 94.04% mean accuracy, 0.8968 mean IoU (intersection over union), 0.9899 weighted IoU, and a mean Boundary F1 (BF) contour matching score of 0.9453, using Deepabv3+ with its weights initialized using ResNet-50. Conclusions The developed CAD system model is able to perform fully automatic and accurate diagnosis of COVID-19 along with infected region extraction and disease stage identification. The ORB image descriptor with bag of features technique and PNN classifier achieved the superior classification performance.
Collapse
Affiliation(s)
- Mohammad H Alshayeji
- Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, P.O. Box 5969, 13060, Safat, Kuwait City, Kuwait.
| | | | - Sa'ed Abed
- Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, P.O. Box 5969, 13060, Safat, Kuwait City, Kuwait
| |
Collapse
|
26
|
Han X, Yu Z, Zhuo Y, Zhao B, Ren Y, Lamm L, Xue X, Feng J, Marr C, Shan F, Peng T, Zhang XY. The value of longitudinal clinical data and paired CT scans in predicting the deterioration of COVID-19 revealed by an artificial intelligence system. iScience 2022; 25:104227. [PMID: 35434542 PMCID: PMC8989658 DOI: 10.1016/j.isci.2022.104227] [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: 12/17/2021] [Revised: 03/10/2022] [Accepted: 04/05/2022] [Indexed: 01/09/2023] Open
Abstract
The respective value of clinical data and CT examinations in predicting COVID-19 progression is unclear, because the CT scans and clinical data previously used are not synchronized in time. To address this issue, we collected 119 COVID-19 patients with 341 longitudinal CT scans and paired clinical data, and we developed an AI system for the prediction of COVID-19 deterioration. By combining features extracted from CT and clinical data with our system, we can predict whether a patient will develop severe symptoms during hospitalization. Complementary to clinical data, CT examinations show significant add-on values for the prediction of COVID-19 progression in the early stage of COVID-19, especially in the 6th to 8th day after the symptom onset, indicating that this is the ideal time window for the introduction of CT examinations. We release our AI system to provide clinicians with additional assistance to optimize CT usage in the clinical workflow. COVID-19 patients with 341 longitudinal CT scans and paired clinical data included A new AI model for the prediction of COVID-19 progression was developed CT scans show significant add-on value over clinical data for the prediction Day 6–8 after the onset of COVID-19 symptoms is an ideal time window for a CT scan
Collapse
Affiliation(s)
- Xiaoyang Han
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China
| | - Ziqi Yu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China
| | - Yaoyao Zhuo
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.,Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Botao Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China
| | - Yan Ren
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200433, China
| | - Lorenz Lamm
- Institute of AI for Health, Helmholtz Zentrum München, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany.,Helmholtz AI, Helmholtz Zentrum München, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Xiangyang Xue
- Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China
| | - Carsten Marr
- Institute of AI for Health, Helmholtz Zentrum München, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Tingying Peng
- Helmholtz AI, Helmholtz Zentrum München, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Xiao-Yong Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| |
Collapse
|
27
|
A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data. Sci Rep 2022; 12:4329. [PMID: 35288579 PMCID: PMC8919158 DOI: 10.1038/s41598-022-07890-1] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 02/22/2022] [Indexed: 01/08/2023] Open
Abstract
AbstractCOVID-19 clinical presentation and prognosis are highly variable, ranging from asymptomatic and paucisymptomatic cases to acute respiratory distress syndrome and multi-organ involvement. We developed a hybrid machine learning/deep learning model to classify patients in two outcome categories, non-ICU and ICU (intensive care admission or death), using 558 patients admitted in a northern Italy hospital in February/May of 2020. A fully 3D patient-level CNN classifier on baseline CT images is used as feature extractor. Features extracted, alongside with laboratory and clinical data, are fed for selection in a Boruta algorithm with SHAP game theoretical values. A classifier is built on the reduced feature space using CatBoost gradient boosting algorithm and reaching a probabilistic AUC of 0.949 on holdout test set. The model aims to provide clinical decision support to medical doctors, with the probability score of belonging to an outcome class and with case-based SHAP interpretation of features importance.
Collapse
|
28
|
Cau R, Faa G, Nardi V, Balestrieri A, Puig J, Suri JS, SanFilippo R, Saba L. Long-COVID diagnosis: From diagnostic to advanced AI-driven models. Eur J Radiol 2022; 148:110164. [PMID: 35114535 PMCID: PMC8791239 DOI: 10.1016/j.ejrad.2022.110164] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/11/2022] [Accepted: 01/13/2022] [Indexed: 12/19/2022]
Abstract
SARS-COV 2 is recognized to be responsible for a multi-organ syndrome. In most patients, symptoms are mild. However, in certain subjects, COVID-19 tends to progress more severely. Most of the patients infected with SARS-COV2 fully recovered within some weeks. In a considerable number of patients, like many other viral infections, various long-lasting symptoms have been described, now defined as "long COVID-19 syndrome". Given the high number of contagious over the world, it is necessary to understand and comprehend this emerging pathology to enable early diagnosis and improve patents outcomes. In this scenario, AI-based models can be applied in long-COVID-19 patients to assist clinicians and at the same time, to reduce the considerable impact on the care and rehabilitation unit. The purpose of this manuscript is to review different aspects of long-COVID-19 syndrome from clinical presentation to diagnosis, highlighting the considerable impact that AI can have.
Collapse
Affiliation(s)
- Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato, Cagliari 09045, Italy
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari, Italy
| | - Valentina Nardi
- Department of Cardiovascular Medicine Mayo Clinic, Rochester, MN, USA
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato, Cagliari 09045, Italy
| | - Josep Puig
- Department of Radiology (IDI), Hospital Universitari de Girona, Girona, Spain
| | - Jasjit S Suri
- Stroke Diagnosis and Monitoring Division, Atheropoint LLC, Roseville, CA, USA
| | - Roberto SanFilippo
- Department of Vascular Surgery, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato, Cagliari 09045, Italy
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato, Cagliari 09045, Italy.
| |
Collapse
|
29
|
Yamada D, Ohde S, Imai R, Ikejima K, Matsusako M, Kurihara Y. Visual classification of three computed tomography lung patterns to predict prognosis of COVID-19: a retrospective study. BMC Pulm Med 2022; 22:1. [PMID: 34980061 PMCID: PMC8721943 DOI: 10.1186/s12890-021-01813-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 12/22/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Quantitative evaluation of radiographic images has been developed and suggested for the diagnosis of coronavirus disease 2019 (COVID-19). However, there are limited opportunities to use these image-based diagnostic indices in clinical practice. Our aim in this study was to evaluate the utility of a novel visually-based classification of pulmonary findings from computed tomography (CT) images of COVID-19 patients with the following three patterns defined: peripheral, multifocal, and diffuse findings of pneumonia. We also evaluated the prognostic value of this classification to predict the severity of COVID-19. METHODS This was a single-center retrospective cohort study of patients hospitalized with COVID-19 between January 1st and September 30th, 2020, who presented with suspicious findings on CT lung images at admission (n = 69). We compared the association between the three predefined patterns (peripheral, multifocal, and diffuse), admission to the intensive care unit, tracheal intubation, and death. We tested quantitative CT analysis as an outcome predictor for COVID-19. Quantitative CT analysis was performed using a semi-automated method (Thoracic Volume Computer-Assisted Reading software, GE Health care, United States). Lungs were divided by Hounsfield unit intervals. Compromised lung (%CL) volume was the sum of poorly and non-aerated volumes (- 500, 100 HU). We collected patient clinical data, including demographic and clinical variables at the time of admission. RESULTS Patients with a diffuse pattern were intubated more frequently and for a longer duration than patients with a peripheral or multifocal pattern. The following clinical variables were significantly different between the diffuse pattern and peripheral and multifocal groups: body temperature (p = 0.04), lymphocyte count (p = 0.01), neutrophil count (p = 0.02), c-reactive protein (p < 0.01), lactate dehydrogenase (p < 0.01), Krebs von den Lungen-6 antigen (p < 0.01), D-dimer (p < 0.01), and steroid (p = 0.01) and favipiravir (p = 0.03) administration. CONCLUSIONS Our simple visual assessment of CT images can predict the severity of illness, a resulting decrease in respiratory function, and the need for supplemental respiratory ventilation among patients with COVID-19.
Collapse
Affiliation(s)
- Daisuke Yamada
- Department of Radiology, St. Luke's International Hospital, 9-1 Akashi-cho, Chuo-ku, Tokyo, 104-8560, Japan.
| | - Sachiko Ohde
- Graduate School of Public Health, St. Luke's International University, 9-1 Akashi-cho, Chuo-ku, Tokyo, 104-8560, Japan
| | - Ryosuke Imai
- Department of Pulmonary Medicine, Thoracic Center, St. Luke's International Hospital, 9-1 Akashi-cho, Chuo-ku, Tokyo, 104-8560, Japan
| | - Kengo Ikejima
- Department of Radiology, St. Luke's International Hospital, 9-1 Akashi-cho, Chuo-ku, Tokyo, 104-8560, Japan
| | - Masaki Matsusako
- Department of Radiology, St. Luke's International Hospital, 9-1 Akashi-cho, Chuo-ku, Tokyo, 104-8560, Japan
| | - Yasuyuki Kurihara
- Department of Radiology, St. Luke's International Hospital, 9-1 Akashi-cho, Chuo-ku, Tokyo, 104-8560, Japan
| |
Collapse
|
30
|
Fusco R, Grassi R, Granata V, Setola SV, Grassi F, Cozzi D, Pecori B, Izzo F, Petrillo A. Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment. J Pers Med 2021; 11:993. [PMID: 34683133 PMCID: PMC8540782 DOI: 10.3390/jpm11100993] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/22/2021] [Accepted: 09/28/2021] [Indexed: 12/26/2022] Open
Abstract
OBJECTIVE To report an overview and update on Artificial Intelligence (AI) and COVID-19 using chest Computed Tomography (CT) scan and chest X-ray images (CXR). Machine Learning and Deep Learning Approaches for Diagnosis and Treatment were identified. METHODS Several electronic datasets were analyzed. The search covered the years from January 2019 to June 2021. The inclusion criteria were studied evaluating the use of AI methods in COVID-19 disease reporting performance results in terms of accuracy or precision or area under Receiver Operating Characteristic (ROC) curve (AUC). RESULTS Twenty-two studies met the inclusion criteria: 13 papers were based on AI in CXR and 10 based on AI in CT. The summarized mean value of the accuracy and precision of CXR in COVID-19 disease were 93.7% ± 10.0% of standard deviation (range 68.4-99.9%) and 95.7% ± 7.1% of standard deviation (range 83.0-100.0%), respectively. The summarized mean value of the accuracy and specificity of CT in COVID-19 disease were 89.1% ± 7.3% of standard deviation (range 78.0-99.9%) and 94.5 ± 6.4% of standard deviation (range 86.0-100.0%), respectively. No statistically significant difference in summarized accuracy mean value between CXR and CT was observed using the Chi square test (p value > 0.05). CONCLUSIONS Summarized accuracy of the selected papers is high but there was an important variability; however, less in CT studies compared to CXR studies. Nonetheless, AI approaches could be used in the identification of disease clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, COVID-19 diagnosis, and disease management.
Collapse
Affiliation(s)
- Roberta Fusco
- IGEA SpA Medical Division—Oncology, Via Casarea 65, Casalnuovo di Napoli, 80013 Naples, Italy;
| | - Roberta Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80138 Naples, Italy; (R.G.); (F.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
| | - Francesca Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80138 Naples, Italy; (R.G.); (F.G.)
| | - Diletta Cozzi
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134 Florence, Italy;
| | - Biagio Pecori
- Division of Radiotherapy and Innovative Technologies, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Francesco Izzo
- Division of Hepatobiliary Surgery, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
| |
Collapse
|
31
|
Asada K, Komatsu M, Shimoyama R, Takasawa K, Shinkai N, Sakai A, Bolatkan A, Yamada M, Takahashi S, Machino H, Kobayashi K, Kaneko S, Hamamoto R. Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics. J Pers Med 2021; 11:886. [PMID: 34575663 PMCID: PMC8471764 DOI: 10.3390/jpm11090886] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 09/01/2021] [Accepted: 09/02/2021] [Indexed: 12/12/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic began at the end of December 2019, giving rise to a high rate of infections and causing COVID-19-associated deaths worldwide. It was first reported in Wuhan, China, and since then, not only global leaders, organizations, and pharmaceutical/biotech companies, but also researchers, have directed their efforts toward overcoming this threat. The use of artificial intelligence (AI) has recently surged internationally and has been applied to diverse aspects of many problems. The benefits of using AI are now widely accepted, and many studies have shown great success in medical research on tasks, such as the classification, detection, and prediction of disease, or even patient outcome. In fact, AI technology has been actively employed in various ways in COVID-19 research, and several clinical applications of AI-equipped medical devices for the diagnosis of COVID-19 have already been reported. Hence, in this review, we summarize the latest studies that focus on medical imaging analysis, drug discovery, and therapeutics such as vaccine development and public health decision-making using AI. This survey clarifies the advantages of using AI in the fight against COVID-19 and provides future directions for tackling the COVID-19 pandemic using AI techniques.
Collapse
Affiliation(s)
- Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ryo Shimoyama
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ken Takasawa
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Norio Shinkai
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Akira Sakai
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Amina Bolatkan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Masayoshi Yamada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of Endoscopy, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
| | - Satoshi Takahashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Kazuma Kobayashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Syuzo Kaneko
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ryuji Hamamoto
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| |
Collapse
|