Trustworthy and Uncertainty-Aware AI for Predicting Respiratory Complications Following Total Hip and Knee Arthroplasty.

Abstract

Total hip and knee arthroplasty (THA/TKA) are among the fastest-growing surgeries in the United States, where they are designed to restore mobility and improve quality of life in individuals with joint disorders. Despite their benefits, these procedures may carry significant risks, including, but not limited to, major respiratory complications. Prompt identification of patients at increased risk is essential for optimizing preoperative treatment, reducing adverse outcomes, and increasing patient safety. In this study, we propose an uncertainty-aware and trustworthy artificial intelligence (AI) framework to predict the likelihood of major respiratory complications, including unplanned intubation, failure to wean from ventilation, and postoperative pneumonia occurring during the index hospitalization and within 30 days following both primary and revision THA and TKA procedures. Unlike traditional risk models, our framework explicitly quantifies prediction uncertainty while maintaining high interpretability, enabling proactive and personalized clinical interventions. We assessed four machine learning (ML) models, including Random Forest (RF), XGBoost (XGB), Logistic Regression (LR), and Artificial Neural Networks (ANNs) to predict three postoperative respiratory outcomes. The ML models demonstrated strong predictive performance, with RF achieving an F1-score of 0.87 for respiratory complications in THA, while ANNs outperformed other models in TKA, also attaining an F1-score of 0.87.

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