Key result
AI and machine learning outperform standard risk scores for TAVI mortality, achieving ~0.79 AUC discrimination.
Why the study?
AI tools and machine-learning models show promise for optimizing procedural planning, guiding decision-making, and predicting complications and mortality in TAVR/TAVI, but require further validation.
Do artificial intelligence tools improve procedural planning, risk stratification, and outcome prediction in patients undergoing transcatheter aortic valve replacement?
Systematic Review (n=51)
Do artificial intelligence tools improve procedural planning, risk stratification, and outcome prediction in patients undergoing transcatheter aortic valve replacement?
Artificial intelligence tools show significant promise in optimizing TAVI procedural planning, enhancing imaging accuracy, and outperforming traditional risk scores in predicting post-procedural outcomes, though challenges in generalizability and transparency remain.
May refine TAVI risk stratification now; extends traditional scores with superior discrimination in Level 1 evidence.
INTRODUCTION: Artificial intelligence (AI) tools have emerged in cardiovascular clinical practice. Regarding transcatheter aortic valve replacement/implantation (TAVR/TAVI) procedures, their utilization optimizes procedural planning, aids physicians with decision making, and predicts possible post-procedural complications. Moreover, machine-learning (ML) models, compared with traditional mortality risk scores, show promising results considering predicted mortality in TAVI patients. However, further validation is required. As the implementation of cardiovascular procedures can be challenging, AI technology broadens the armamentarium of tools that a clinician is able to use for a more comprehensive evaluation of patients, minimizing complications and resulting in optimum clinical outcomes. METHODS: A comprehensive literature search was conducted through the PubMed and Google Scholar databases from inception to 20 September 2025, to identify relevant studies. The search strategy included the following keywords: ["TAVI" OR "TAVR"] AND ["AI", Artificial Intelligence]. RESULTS: According to our database research, 7177 articles were initially screened, and 2145 duplicate articles were excluded. Eventually, 189 articles were evaluated by our reviewers and 51 articles of studies published between 2014 and 2025 were included in our review. CONCLUSIONS: AI algorithms could revolutionize the Heart Team decision making process, being not only a tool for patient evaluation but an active member of the team with applications to analyze and optimize all stages of the TAVI procedure, guide decision making and predict outcomes, and, with the contribution and evaluation of information from all human members of the team, enhance even more the patient-mediated medicine/interventions.
No takes yet. Share an insight, caveat, or question.
Tsakirian et al. (2025) conducted a systematic review in Aortic Valve Stenosis undergoing TAVR/TAVI (n=51). Artificial Intelligence and Machine Learning vs. Traditional clinical risk scores and manual imaging assessment was evaluated. Artificial intelligence and machine learning models consistently outperformed traditional risk scores for predicting mortality in TAVI patients, achieving a pooled discrimination AUC of 0.78 to 0.79.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: