Published on in Vol 22 , No 6 (2020) :June
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/19569, first published
.

Journals
- Ozsahin I, Sekeroglu B, Musa M, Mustapha M, Uzun Ozsahin D. Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence. Computational and Mathematical Methods in Medicine 2020;2020:1 View
- Tayarani N. M. Applications of artificial intelligence in battling against covid-19: A literature review. Chaos, Solitons & Fractals 2021;142:110338 View
- Wang S, Govindaraj V, Górriz J, Zhang X, Zhang Y. Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network. Information Fusion 2021;67:208 View
- Owais M, Arsalan M, Mahmood T, Kang J, Park K. Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning–Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation. Journal of Medical Internet Research 2020;22(11):e18563 View
- Syeda H, Syed M, Sexton K, Syed S, Begum S, Syed F, Prior F, Yu Jr F. Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review. JMIR Medical Informatics 2021;9(1):e23811 View
- Yoo S, Goo J, Yoon S. Role of Chest Radiographs and CT Scans and the Application of Artificial Intelligence in Coronavirus Disease 2019. Journal of the Korean Society of Radiology 2020;81(6):1334 View
- Wei C. Research on university laboratory management and maintenance framework based on computer aided technology. Microprocessors and Microsystems 2020:103617 View
- Sitaula C, Hossain M. Attention-based VGG-16 model for COVID-19 chest X-ray image classification. Applied Intelligence 2021;51(5):2850 View
- Wang S, Nayak D, Guttery D, Zhang X, Zhang Y. COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis. Information Fusion 2021;68:131 View
- Li D, Zhang Q, Tan Y, Feng X, Yue Y, Bai Y, Li J, Li J, Xu Y, Chen S, Xiao S, Sun M, Li X, Zhu F. Prediction of COVID-19 Severity Using Chest Computed Tomography and Laboratory Measurements: Evaluation Using a Machine Learning Approach. JMIR Medical Informatics 2020;8(11):e21604 View
- Xu M, Ouyang L, Han L, Sun K, Yu T, Li Q, Tian H, Safarnejad L, Zhang H, Gao Y, Bao F, Chen Y, Robinson P, Ge Y, Zhu B, Liu J, Chen S. Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach. Journal of Medical Internet Research 2021;23(1):e25535 View
- Elmuogy S, Hikal N, Hassan E. An efficient technique for CT scan images classification of COVID-19. Journal of Intelligent & Fuzzy Systems 2021;40(3):5225 View
- Ho T, Park J, Kim T, Park B, Lee J, Kim J, Kim K, Choi S, Kim Y, Lim J, Choi S. Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study. JMIR Medical Informatics 2021;9(1):e24973 View
- Homayounieh F, Bezerra Cavalcanti Rockenbach M, Ebrahimian S, Doda Khera R, Bizzo B, Buch V, Babaei R, Karimi Mobin H, Mohseni I, Mitschke M, Zimmermann M, Durlak F, Rauch F, Digumarthy S, Kalra M. Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome. Journal of Digital Imaging 2021;34(2):320 View
- Abbasi W, Abbas S, Andleeb S, ul Islam G, Ajaz S, Arshad K, Khalil S, Anjam A, Ilyas K, Saleem M, Chughtai J, Abbas A. COVIDC: An expert system to diagnose COVID-19 and predict its severity using chest CT scans: Application in radiology. Informatics in Medicine Unlocked 2021;23:100540 View
- Rasheed J, Jamil A, Hameed A, Al-Turjman F, Rasheed A. COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review. Interdisciplinary Sciences: Computational Life Sciences 2021;13(2):153 View
- Mohammad-Rahimi H, Nadimi M, Ghalyanchi-Langeroudi A, Taheri M, Ghafouri-Fard S. Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review. Frontiers in Cardiovascular Medicine 2021;8 View
- Han C, Kim M, Kwak J, Ortega-Martorell S. Semi-supervised learning for an improved diagnosis of COVID-19 in CT images. PLOS ONE 2021;16(4):e0249450 View
- Glangetas A, Hartley M, Cantais A, Courvoisier D, Rivollet D, Shama D, Perez A, Spechbach H, Trombert V, Bourquin S, Jaggi M, Barazzone-Argiroffo C, Gervaix A, Siebert J. Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case–control and prospective cohort study. BMC Pulmonary Medicine 2021;21(1) View
- Roberts M, Driggs D, Thorpe M, Gilbey J, Yeung M, Ursprung S, Aviles-Rivero A, Etmann C, McCague C, Beer L, Weir-McCall J, Teng Z, Gkrania-Klotsas E, Rudd J, Sala E, Schönlieb C. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nature Machine Intelligence 2021;3(3):199 View
- Ghaderzadeh M, Asadi F, Maietta S. Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review. Journal of Healthcare Engineering 2021;2021:1 View
- Chung H, Ko H, Kang W, Kim K, Lee H, Park C, Song H, Choi T, Seo J, Lee J. Prediction and Feature Importance Analysis for Severity of COVID-19 in South Korea Using Artificial Intelligence: Model Development and Validation. Journal of Medical Internet Research 2021;23(4):e27060 View
- Moezzi M, Shirbandi K, Shahvandi H, Arjmand B, Rahim F. The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis. Informatics in Medicine Unlocked 2021;24:100591 View
- Adamidi E, Mitsis K, Nikita K. Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review. Computational and Structural Biotechnology Journal 2021;19:2833 View
- Helwan A, Ma’aitah M, Hamdan H, Ozsahin D, Tuncyurek O, Bangyal W. Radiologists versus Deep Convolutional Neural Networks: A Comparative Study for Diagnosing COVID-19. Computational and Mathematical Methods in Medicine 2021;2021:1 View
- Alhasan M, Hasaneen M. Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic. Computerized Medical Imaging and Graphics 2021;91:101933 View
- Athavale A, Hart P, Itteera M, Cimbaluk D, Patel T, Alabkaa A, Arruda J, Singh A, Rosenberg A, Kulkarni H. Development and Validation of a Deep Learning Model to Quantify Interstitial Fibrosis and Tubular Atrophy From Kidney Ultrasonography Images. JAMA Network Open 2021;4(5):e2111176 View
- Kumar V, Singh D, Kaur M, Damaševičius R. Overview of current state of research on the application of artificial intelligence techniques for COVID-19. PeerJ Computer Science 2021;7:e564 View
- Rehouma R, Buchert M, Chen Y. Machine learning for medical imaging‐based COVID‐19 detection and diagnosis. International Journal of Intelligent Systems 2021;36(9):5085 View
- Santosh K, Ghosh S. Covid-19 Imaging Tools: How Big Data is Big?. Journal of Medical Systems 2021;45(7) View
- Oyelade O, Ezugwu A, Chiroma H. CovFrameNet: An Enhanced Deep Learning Framework for COVID-19 Detection. IEEE Access 2021;9:77905 View
- Kato S, Ishiwata Y, Aoki R, Iwasawa T, Hagiwara E, Ogura T, Utsunomiya D. Imaging of COVID-19: An update of current evidences. Diagnostic and Interventional Imaging 2021 View
- Arora V, Ng E, Leekha R, Darshan M, Singh A. Transfer learning-based approach for detecting COVID-19 ailment in lung CT scan. Computers in Biology and Medicine 2021;135:104575 View
- CANBAY Y, İSMETOĞLU A, CANBAY P. COVİD-19 HASTALIĞININ TEŞHİSİNDE DERİN ÖĞRENME VE VERİ MAHREMİYETİ. Mühendislik Bilimleri ve Tasarım Dergisi 2021;9(2):701 View
- Dey S, Bhattacharya R, Malakar S, Mirjalili S, Sarkar R. Choquet fuzzy integral-based classifier ensemble technique for COVID-19 detection. Computers in Biology and Medicine 2021;135:104585 View
- Rahman M, Nooruddin S, Hasan K, Dey N. HOG + CNN Net: Diagnosing COVID-19 and Pneumonia by Deep Neural Network from Chest X-Ray Images. SN Computer Science 2021;2(5) View
- Hasan N. A Hybrid Method of Covid-19 Patient Detection from Modified CT-Scan/Chest-X-Ray Images Combining Deep Convolutional Neural Network And Two- Dimensional Empirical Mode Decomposition. Computer Methods and Programs in Biomedicine Update 2021;1:100022 View
- Karthik R, Menaka R, Hariharan M, Kathiresan G. AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions. IRBM 2021 View
- Wang S, Satapathy S, Anderson D, Chen S, Zhang Y. Deep Fractional Max Pooling Neural Network for COVID-19 Recognition. Frontiers in Public Health 2021;9 View