Machine learning models outperform traditional scoring methods in predicting post-TAVR complications, achieving AUCs greater than 0.80 compared to 0.72 or less for conventional tools.
Does machine learning improve preoperative anatomical assessment and postoperative risk prediction compared to conventional approaches in patients undergoing TAVR?
Machine learning models demonstrate non-inferior accuracy and significantly faster analysis times for TAVR anatomical measurements, while outperforming traditional risk scores in predicting postoperative complications.
This study aimed to investigate the application of machine learning (ML) in transcatheter aortic valve replacement (TAVR) and to demonstrate that, owing to the unique strengths of ML, this field outperforms conventional approaches in both preoperative assessment and postoperative prediction of TAVR. Nonetheless, TAVR is the preferred treatment option for medium- and high-risk patients with aortic stenosis, a common valvular disease, because of the associated minimally invasive nature and rapid recovery. However, challenges remain in preoperative evaluation and in predicting postoperative complications. Thus, ML technology offers innovative solutions for these challenges. This study provides an overview of current ML applications in TAVR and evaluates the associated benefits in measuring preoperative anatomical parameters and predicting postoperative complications. Indeed, the superiority of ML models for preoperative planning can be assessed by comparing ML model-derived data with measurements from senior and junior observers across various aortic root anatomical parameters. Additionally, this review discusses the challenges of applying ML in TAVR, including data acquisition, privacy protection, and model generalizability. The ongoing advancement of artificial intelligence (AI) technologies, particularly the integration of explainable AI and federated learning, is expected to enhance the accuracy and personalization of preoperative planning and postoperative prediction for TAVR. This progress will facilitate broader application of these technologies, ultimately benefiting a wider patient population.
Hu et al. (Fri,) conducted a review in Aortic stenosis requiring Transcatheter Aortic Valve Replacement (TAVR). Machine Learning (ML) vs. Traditional risk scores and manual anatomical measurements was evaluated on Accuracy of anatomical assessment and postoperative complication prediction. Machine learning models outperform traditional scoring methods in predicting post-TAVR complications, achieving AUCs greater than 0.80 compared to 0.72 or less for conventional tools.