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AI-Driven Integration of Deep Learning with Lung Imaging, Functional Analysis, and Blood Gas Metrics for Perioperative Hypoxemia Prediction: Progress and Perspectives
kecheng Huang;
Chunjun Wu;
Jieyu Fang;
Rong Pi
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.
Citation
Please cite as:
Huang k, Wu C, Fang J, Pi R
AI-Driven Integration of Deep Learning With Lung Imaging, Functional Analysis, and Blood Gas Metrics for Perioperative Hypoxemia Prediction