Key result
Fully automated AI-based CT analysis demonstrated excellent agreement with semi-automated measurements for aortic annulus dimensions, though simulated prosthesis size selection differed from the implanted size in 21% of patients compared to 14% with the semi-automated method.
Why the study?
Fully automated, artificial intelligence-based CT analysis platforms have been developed for TAVI planning, but their clinical impact on valve prosthesis size selection required investigation.
Does fully-automated AI-based CT analysis provide comparable annular measurements and prosthesis size selection to semi-automated analysis in patients with symptomatic severe aortic stenosis undergoing TAVI?
Observational (n=247)
Yes
Does fully-automated AI-based CT analysis provide comparable annular measurements and prosthesis size selection to semi-automated analysis in patients with symptomatic severe aortic stenosis undergoing TAVI?
Answers are grounded in this paper and cite their sources.
Fully automated AI-based CT analysis demonstrates excellent agreement with semi-automated methods for TAVI annular sizing, though expert interpretation remains necessary for final prosthesis selection.
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 mm 2 vs. 440.9 ± 85.6 mm 2 ; MAE: 21.2 mm 2 ; 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.
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Arsalan et al. (2026) conducted an observational in Symptomatic severe aortic stenosis (n=247). Fully-automated AI-based CT-analysis (heart.ai) vs. Semi-automated CT-analysis (3mensio) was evaluated on Discordance in simulated prosthesis size selection compared to the actually implanted valve size. Fully automated AI-based CT analysis demonstrated excellent agreement with semi-automated measurements for aortic annulus dimensions, though simulated prosthesis size selection differed from the implanted size in 21% of patients compared to 14% with the semi-automated method.
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