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Accurate planning of transcatheter aortic implantation (TAVI) is important tominimize complications, and it requires anatomic evaluation of the aortic root(AR), commonly done through 3D computed tomography (CT) image analysis.Currently, there is no standard automated solution for this process. Twoconvolutional neural networks (CNNs) with 3D U-Net architectures (model 1 andmodel 2) were trained on 310 CT scans for AR analysis. Model 1 performed ARsegmentation and model 2 identified the aortic annulus and sinotubular junction(STJ) contours. Results were validated against manual measurements of 178 TAVIcandidates. After training, the two models were integrated into a fullyautomated pipeline for geometric analysis of the AR. The trained CNNseffectively segmented the AR, annulus and STJ, resulting in mean Dice scores of0.93 for the AR, and mean surface distances of 1.16 mm and 1.30 mm for theannulus and STJ, respectively. Automatic measurements were in good agreementwith manual annotations, yielding annulus diameters that differed by 0.52[-2.96, 4.00] mm (bias and 95% limits of agreement for manual minus algorithm).Evaluating the area-derived diameter, bias and limits of agreement were 0.07[-0.25, 0.39] mm. STJ and sinuses diameters computed by the automatic methodyielded differences of 0.16 [-2.03, 2.34] and 0.1 [-2.93, 3.13] mm,respectively. The proposed tool is a fully automatic solution to quantifymorphological biomarkers for pre-TAVI planning. The method was validatedagainst manual annotation from clinical experts and showed to be quick andeffective in assessing AR anatomy, with potential for time and cost savings.

A CT-based deep learning system for automatic assessment of aortic root morphology for TAVI planning