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

Theme Issue: Medical Informatics and COVID-19 (107) JMIR Theme Issue: COVID-19 Special Issue (2457) Machine Learning (1488) Decision Support for Health Professionals (1201) Artificial Intelligence (1407) Imaging Informatics (198)

Published on in Vol 9, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25181, first published .
Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review

Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review

Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review

Authors of this article:

Mahdieh Montazeri1 Author Orcid Image ;   Roxana ZahediNasab2 Author Orcid Image ;   Ali Farahani2 Author Orcid Image ;   Hadis Mohseni2 Author Orcid Image ;   Fahimeh Ghasemian2 Author Orcid Image
Article Authors Cited by (18) Tweetations (11) Metrics

Journals

  1. Nneji G, Cai J, Monday H, Hossin M, Nahar S, Mgbejime G, Deng J. Fine-Tuned Siamese Network with Modified Enhanced Super-Resolution GAN Plus Based on Low-Quality Chest X-ray Images for COVID-19 Identification. Diagnostics 2022;12(3):717 View
  2. Monday H, Li J, Nneji G, Nahar S, Hossin M, Jackson J, Ejiyi C. COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network. Diagnostics 2022;12(3):741 View
  3. Ghosheh G, Alamad B, Yang K, Syed F, Hayat N, Iqbal I, Al Kindi F, Al Junaibi S, Al Safi M, Ali R, Zaher W, Al Harbi M, Shamout F. Clinical prediction system of complications among patients with COVID-19: A development and validation retrospective multicentre study during first wave of the pandemic. Intelligence-Based Medicine 2022;6:100065 View
  4. Carrillo-Larco R, Castillo-Cara M, Hernández Santa Cruz J. Street images classification according to COVID-19 risk in Lima, Peru: a convolutional neural networks feasibility analysis. BMJ Open 2022;12(9):e063411 View
  5. Suraci C, De Angelis V, Lofaro G, Giudice M, Marrara G, Rinaldi F, Russo A, Bevacqua M, Lax G, Mammone N, Laboccetta A, Morabito F, Araniti G. The Next Generation of eHealth: A Multidisciplinary Survey. IEEE Access 2022;10:134623 View
  6. Ormeño P, Márquez G, Guerrero-Nancuante C, Taramasco C. Detection of COVID-19 Patients Using Machine Learning Techniques: A Nationwide Chilean Study. International Journal of Environmental Research and Public Health 2022;19(13):8058 View
  7. Montazeri M, Montazeri M, Bahaadinbeigy K, Montazeri M, Afraz A. Application of machine learning methods in predicting schizophrenia and bipolar disorders: A systematic review. Health Science Reports 2023;6(1) View
  8. Lasker A, Obaidullah S, Chakraborty C, Roy K. Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review. SN Computer Science 2022;4(1) View
  9. Lee M, Shomanov A, Kudaibergenova M, Viderman D. Deep Learning Methods for Interpretation of Pulmonary CT and X-ray Images in Patients with COVID-19-Related Lung Involvement: A Systematic Review. Journal of Clinical Medicine 2023;12(10):3446 View
  10. Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior K, Poirrier J, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Review of Pharmacoeconomics & Outcomes Research 2024;24(1):63 View
  11. 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. Journal of Medical Internet Research 2023;25:e46340 View
  12. Liu R, Wu S, Yu H, Zeng K, Liang Z, Li S, Hu Y, Yang Y, Ye L. Prediction model for hepatocellular carcinoma recurrence after hepatectomy: Machine learning-based development and interpretation study. Heliyon 2023;9(11):e22458 View
  13. Talimtzi P, Ntolkeras A, Kostopoulos G, Bougioukas K, Pagkalidou E, Ouranidis A, Pataka A, Haidich A. The reporting completeness and transparency of systematic reviews of prognostic prediction models for COVID-19 was poor: a methodological overview of systematic reviews. Journal of Clinical Epidemiology 2024;167:111264 View
  14. Gromiha M, Preethi P, Pandey M. From Code to Cure: The Impact of Artificial Intelligence in Biomedical Applications. BioMedInformatics 2024;4(1):542 View
  15. Fu Y, Xu X, Du J, Huang T, Shi J, Song G, Gu Q, Shen H, Wang S. Using machine learning algorithms based on patient admission laboratory parameters to predict adverse outcomes in COVID-19 patients. Heliyon 2024;10(9):e29981 View
  16. Hsu J, Singaravelan A, Lai C, Li Z, Lin C, Wu W, Kao T, Chu P. Applying a Deep Learning Model for Total Kidney Volume Measurement in Autosomal Dominant Polycystic Kidney Disease. Bioengineering 2024;11(10):963 View

Books/Policy Documents

  1. Vavougios G, Zarogiannis S, Gourgoulianis K. Omics approaches and technologies in COVID-19. View
  2. Roberts M, Frank O, Bagon S, Eldar Y, Schönlieb C. Artificial Intelligence in Covid-19. View