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

Reviews in Medical Informatics (117) Respiratory Medicine (164) Respiratory Medicine (164) Decision Support for Health Professionals (1594) Machine Learning (2173) Decision Support for Health Professionals (1594) Machine Learning (2173)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50642, first published .
Predicting Hypoxia Using Machine Learning: Systematic Review

Predicting Hypoxia Using Machine Learning: Systematic Review

Predicting Hypoxia Using Machine Learning: Systematic Review

Authors of this article:

Lena Pigat1 Author Orcid Image ;   Benjamin P Geisler1 Author Orcid Image ;   Seyedmostafa Sheikhalishahi1 Author Orcid Image ;   Julia Sander1 Author Orcid Image ;   Mathias Kaspar1 Author Orcid Image ;   Maximilian Schmutz1, 2 Author Orcid Image ;   Sven Olaf Rohr1 Author Orcid Image ;   Carl Mathis Wild1, 3 Author Orcid Image ;   Sebastian Goss1 Author Orcid Image ;   Sarra Zaghdoudi1 Author Orcid Image ;   Ludwig Christian Hinske1, 4 Author Orcid Image
Article Authors Cited by (5) Tweetations (5) Metrics

Journals

  1. Pardo E, Le Cam E, Verdonk F. Artificial intelligence and nonoperating room anesthesia. Current Opinion in Anaesthesiology 2024;37(4):413 View
  2. Nanini S, Abid M, Mamouni Y, Wiedemann A, Jouvet P, Bourassa S. Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies. Diagnostics 2024;14(23):2763 View
  3. Al-Hajjar S. Advances in Artificial Intelligence in Pediatrics: Current Developments and Future Perspectives. International Journal of Pediatrics and Adolescent Medicine 2024;11(4):89 View
  4. Huang M, Law H, Tam S. Use of Radiomics in Characterizing Tumor Hypoxia. International Journal of Molecular Sciences 2025;26(14):6679 View
  5. Baek S, Park J, Heo J, Kim K, Baek D, Oh C, Lee H, Lee D, Hong B, Kakulapati V. Hypoxemia prediction in pediatric patients under general anesthesia using machine learning: A retrospective observational study and external validation. PLOS One 2026;21(1):e0339276 View