Artificial intelligence applications in TAVR achieved high diagnostic precision (accuracy up to 0.989) and outperformed conventional risk scores for mortality prediction (pooled AUC 0.78-0.79).
Does artificial intelligence improve pre-procedural planning, risk prediction, and outcomes in patients undergoing TAVR?
Artificial intelligence applications in TAVR demonstrate high diagnostic precision for imaging and outperform conventional risk scores for mortality prediction, highlighting their potential to optimize procedural planning and patient outcomes.
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Abstract Background Artificial intelligence (AI) has rapidly expanded into cardiovascular medicine, offering new capabilities in imaging analysis, procedural guidance and risk prediction. Within transcatheter aortic valve replacement (TAVR/TAVI), AI algorithms aim to optimize pre-procedural planning, improve decision-making, and predict complications and outcomes. Despite numerous reports, a comprehensive synthesis of these advances is lacking. Purpose To systematically review published evidence on the application of AI in TAVR/TAVI, summarizing validated models, clinical performance, and future direction for integration into interventional cardiology practice. Methods Following PRISMA guidelines, PubMed and Google Scholar were searched from inception to September 2025 using the keywords "TAVI" or TAVR" and "AI" or "Artificial Intelligence". Studies were included if they evaluated AI or machine-learning tools for pre-procedural imaging, procedural planning, risk stratification, or outcome prediction in TAVR. Abstracts, case reports, and animal studies were excluded. Key data on AI methodology, imaging modality , and clinical endpoints were extracted and narratively synthesized. Results A total of 7,177 studies were screened, 2,145 duplicates were excluded and 189 underwent full-text evaluation. Fifty-one studies met inclusion criteria (2017-2025). AI- driven imaging tools achieved high diagnostic precision for annular sizing and coronary ostia height (accuracy up to 0.989), reducing operator variability and processing time. Deep-learning image reconstruction reduced radiation exposure by 50% while maintaining image quality. Machine- learning models consistently outperformed conventional risk scores (EuroSCORE II, TAVI-SCORE, CoreValve) for mortality prediction, with pooled AUC values of 0.78-0.79. AI models integrating imaging, clinical and biomarker data enhanced prediction of complications, including conduction abnormalities, cerebrovascular events, and vascular injury. Emerging applications such as extended reality (XR)- assisted planning, digital twin modeling and AI- supported telemonitoring (TeleTAVI) demonstrated feasibility for workflow optimization and personalised care. Conclusion AI is poised to revolutionize TAVR by enhancing imaging analysis, risk prediction and procedural precision. Its integration across the full clinical pathway can refine decision-making and improve patient outcomes. However, external validation, ethical governance and standardization of AI reporting remain essential before full clinical adoption. AI should be regarded as an active member of Heart Team, augmenting human expertise to deliver safer, patient centered interventions.
Tsakirian et al. (Sun,) reported a other. Artificial intelligence applications in TAVR achieved high diagnostic precision (accuracy up to 0.989) and outperformed conventional risk scores for mortality prediction (pooled AUC 0.78-0.79).