Why the study?
To retrain an existing deep learning-based fully automated aortic valve leaflets and root measurement algorithm using CT data for root dilatation, and assess its clinical feasibility.
Does a deep learning-based fully automated algorithm accurately and efficiently measure aortic valve leaflets and root geometry from CT images compared to expert manual measurement?
Does a deep learning-based fully automated algorithm accurately and efficiently measure aortic valve leaflets and root geometry from CT images compared to expert manual measurement?
A fully automated deep learning algorithm can accurately and rapidly measure aortic valve and root geometry from CT images, potentially streamlining planning for transcatheter and surgical aortic valve interventions.
May support automated CT root measurements in dilatation; leaves open prospective validation before clinical use.
BACKGROUND: The aim of this study was to retrain our existing deep learning-based fully automated aortic valve leaflets/root measurement algorithm, using computed tomography (CT) data for root dilatation (RD), and assess its clinical feasibility. METHODS AND RESULTS: for the virtual basal ring area, 1.1-2.6 mm for sinus diameter, 0.1-0.6 mm for coronary artery height, 0.2-0.5 mm for geometric height, and 0.9 mm for effective height, except for the sinotubular junction of the AR cases (10.3 mm) with an indefinite borderline over the dilated sinuses, compared with 2.1 mm in AS cases. The measurement time (122 s) per case by the algorithm was significantly shorter than those of the experts (618-1,126 s). CONCLUSIONS: This fully automated algorithm can assist in evaluating aortic valve/root anatomy for planning surgical and transcatheter treatments while saving time and minimizing workload.
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Yamauchi et al. (2025) studied this question.
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