Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Mar 16, 2025
Open Peer Review Period: Mar 16, 2025 - May 11, 2025
Date Accepted: Aug 4, 2025
Date Submitted to PubMed: Aug 4, 2025
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Advances in AI for Pulmonary Inflammation Recognition and Prediction of Perioperative Hypoxemia: Integrating Deep Learning with Lung Imaging, Functional Analysis, and Blood Gas Metrics
ABSTRACT
Pulmonary inflammation, encompassing pneumonia, COVID-19 sequelae, and chronic obstructive pulmonary disease (COPD), continues to be a predominant cause of perioperative complications, particularly hypoxemia. Artificial intelligence (AI), particularly deep learning (DL), has emerged as a revolutionary tool for the early detection of pulmonary inflammation and proactive risk stratification. This review examines the recent progress in AI-driven analysis of radiological imaging, preoperative PFTs, and ABG parameters for predicting perioperative hypoxemia, while addressing challenges and future directions. As AI keeps evolving, its role in the management of respiratory diseases and prediction of perioperative hypoxemia will ultimately improving global health outcomes.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.