Background: Traditional intraoperative sizing for sutureless aortic valves, such as the Corcym Perceval Plus (CPP), often relies on subjective tactile feedback, which can lead to excessive over-sizing. Significant over-sizing is associated with complications like increased trans-prosthetic gradients, valve thrombosis, and conduction disturbances requiring permanent pacemakers. This study aims to develop an AI-driven predictive recommendation system using Multidetector Computed Tomography (MDCT) data to optimize valve sizing and improve patient outcomes. Methods: Data were collected from 380 consecutive patients who underwent aortic valve replacement with a CPP prosthesis between 2011 and 2026. Two machine learning models were trained using preoperative MDCT features, including annular area, perimeter, and diameters. The first model predicted “normal” clinical labels, while the second used “penalized” labels adjusted for postoperative hemodynamic performance to discourage over-sizing. The dataset was split into training (80%) and testing (20%) subsets. Results: The mean patient age was 77.6 years. The model using normal labels achieved an overall accuracy of 91.84% (68.75% on the test set). The penalized label model showed improved performance with an overall accuracy of 92.89% (72.16% on the test set). MDCT provided highly reproducible objective metrics superior to echocardiography for calculating optimal sizing. Conclusions: The AI-driven recommendation system proves to be a reliable and reproducible tool for preoperative planning. By transitioning from subjective tactile assessment to predictive modeling, surgeons can better select valve sizes that minimize complications, particularly in minimally invasive approaches.
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