Background: Accurate assessment of aortic root is crucial for the preprocedural planning of transcatheter aortic valve replacement (TAVR). A variety software is emerging for the semiautomated or automated measurements during TAVR planning. This study evaluated a new deep-learning (DL) tool based on cardiac computed tomography angiography (CCTA) for fully automatic assessment of aortic root.
Methods: The study included 126 patients with CCTA, 63 of whom underwent TAVR. In the overall population, the DL method was compared to manual measurements of the annulus dimensions. Within the TAVR group, the DL method was also compared to 3mensio software-derived aortic root measure, including the annulus, left ventricular outflow tract (LVOT), sinotubular junction (STJ), ascending aorta (AAo), and the heights of both the coronary ostia.
Results: Data were successfully analyzed using the DL method in 122 (96.8%) of patients. The correlation of annular diameters between the DL and manual methods was good to excellent for the overall cohort (n=118; r=0.83), the TAVR group (n=59, r=0.86), and its subgroups [bicuspid aortic valve (BAV): n=12, r=0.74; tricuspid aortic valve (TAV): n=47, r=0.93]. In the comparison of the DL method with 3mensio, the highest correlation was found for AAo (r=0.99). Among the four diameter indices [minimum, maximum, perimeter-derived diameter (pDD), and area-derived diameter (aDD)], excellent correlation was observed for aDD (LVOT: r=0.92; annulus: r=0.89).
Conclusions: The DL method offers an effective and efficient tool for the quantification of aortic roots for TAVR planning.
Keywords: Quantitative evaluation; cardiac computed tomography angiography (CCTA); deep-learning (DL).
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