Impact of fully-automated AI based CT-analysis on pre-procedural TAVI planning.

BACKGROUND: Accurate pre-procedural computed tomography (CT) analysis is essential for optimal valve sizing and clinical outcomes in transcatheter aortic valve implantation (TAVI). Recently, fully automated, artificial intelligence (AI)-based CT analysis platforms have been developed to simplify and standardize this process. AIMS: The aim of the study was to investigate the clinical impact of this new analysis method on the selection of valve prosthesis size. METHODS: Overall, 247 patients with symptomatic severe aortic stenosis were enrolled. Patients underwent TAVI procedures at two different heart centres. The pre-procedural datasets were analysed by a standard TAVI CT-analysis software (3M, Pie Medical Imaging BV, The Netherlands) and a fully-automated CT-analysis-platform employing a deep-learning based algorithm. Key annular measurements and simulated prosthesis size selection were compared between both methods. RESULTS: The mean aortic annulus diameter was 24.5 ± 2.3 mm (3mensio) and 24.4 ± 2.4 mm (AI), respectively, with a mean absolute error (MAE) of 0.6 mm and mean absolute percentage error (MAPE) of 2.6%. Annulus perimeter (76.9 ± 7.0 mm vs. 74.6 ± 7.3 mm; MAE: 2.0 mm; MAPE: 2.6%) and annulus area (458.4 ± 87.2 mm2 vs. 440.9 ± 85.6 mm2; MAE: 21.2 mm2; MAPE: 4.6%) showed excellent correlation (intraclass correlation coefficients > 0.95). Prosthesis size selection simulated on the basis of AI-derived measurements would have differed from the implanted size in 21% of patients, compared with 14% when using the semi-automated method. CONCLUSIONS: In this retrospective study, fully automated AI-based CT analysis demonstrated excellent agreement with conventional semi-automated measurements of the aortic annulus. Nevertheless, similar to established planning workflows, expert interpretation remains crucial to integrate the broader anatomical and clinical context required for optimal prosthesis size selection.