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 .
COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation

COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation

COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation

Journals

  1. 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
  2. Tayarani N. M. Applications of artificial intelligence in battling against covid-19: A literature review. Chaos, Solitons & Fractals 2021;142:110338 View
  3. 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
  4. 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
  5. 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
  6. 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
  7. Wei C. Research on university laboratory management and maintenance framework based on computer aided technology. Microprocessors and Microsystems 2020:103617 View
  8. Sitaula C, Hossain M. Attention-based VGG-16 model for COVID-19 chest X-ray image classification. Applied Intelligence 2021;51(5):2850 View
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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 View
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. Alhasan M, Hasaneen M. Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic. Computerized Medical Imaging and Graphics 2021;91:101933 View
  27. 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
  28. 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
  29. Rehouma R, Buchert M, Chen Y. Machine learning for medical imaging‐based COVID‐19 detection and diagnosis. International Journal of Intelligent Systems 2021 View
  30. Santosh K, Ghosh S. Covid-19 Imaging Tools: How Big Data is Big?. Journal of Medical Systems 2021;45(7) View
  31. Oyelade O, Ezugwu A, Chiroma H. CovFrameNet: An Enhanced Deep Learning Framework for COVID-19 Detection. IEEE Access 2021;9:77905 View
  32. 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
  33. 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 2021:104575 View

Books/Policy Documents

  1. Sugiura A. Bio-information for Hygiene. View