The Potential Role of an Artificial Intelligence-Driven Tool in Decision-Making for Mitral Valve Repair Surgery.

Background: Annuloplasty ring sizing is critical for durable outcomes in surgical mitral valve repair (MVr). However, there is no clear consensus on optimal sizing strategies. Artificial intelligence (AI)-based imaging tools may help to reduce uncertainty in preoperative decision-making by providing objective, reproducible and reliable measurements. This study evaluated the predictive capability of a fully automated, computed tomography (CT)-based AI-driven tool for annuloplasty ring sizing in patients undergoing minimally invasive MVr (MI-MVr). Methods: A total of 71 consecutive patients undergoing MI-MVr for Carpentier type II mitral valve insufficiency during the study period were included. Preoperative CT scans were analyzed using a cloud-based, fully automated AI tool to quantify mitral valve geometric parameters. Correlations between AI-derived measurements and implanted ring sizes were assessed using the Pearson correlation test. Univariable and multivariable linear regression analyses were performed to identify independent predictors of ring size selection. Results: Several AI-derived parameters correlated significantly with implanted ring size, with the strongest correlations observed for commissural width (R = 0.693, p < 0.001) and mitral annular area (R = 0.693, p < 0.001). In multivariable regression analysis, these parameters were the strongest predictors of annuloplasty ring size (R2 = 0.504, p < 0.001). Using this model, accurate annuloplasty ring sizing could be predicted in 78.8% of patients. There were no in-hospital mortality and residual mitral regurgitation at discharge. Conclusions: A fully automated, CT-based AI-driven tool demonstrated good accuracy for preoperative annuloplasty ring size prediction in MI-MVr and may have the potential to support surgical decision-making, reduce operator dependence, and improve reproducibility.