This paper is in the following e-collection/theme issue:

JMIR Theme Issue 2020/21: COVID-19 Special Issue (974) Decision Support for Health Professionals (349) Imaging Informatics (84) Machine Learning (467) Outbreak and Pandemic Preparedness and Management (736) Theme Issue 2020-2021: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (735)

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

Authors of this article:

Hoon Ko 1 Author Orcid Image ;   Heewon Chung 1 Author Orcid Image ;   Wu Seong Kang 2 Author Orcid Image ;   Kyung Won Kim 3 Author Orcid Image ;   Youngbin Shin 3 Author Orcid Image ;   Seung Ji Kang 4 Author Orcid Image ;   Jae Hoon Lee 5 Author Orcid Image ;   Young Jun Kim 5 Author Orcid Image ;   Nan Yeol Kim 2 Author Orcid Image ;   Hyunseok Jung 6 Author Orcid Image ;   Jinseok Lee 1 Author Orcid Image
Article Authors Cited by (35) Tweetations (32) Metrics

Journals

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  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
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  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
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  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
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Books/Policy Documents

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