Rapid segmentation of computed tomography angiography images of the aortic valve: the efficacy and clinical value of a deep learning algorithm.

Objectives: The goal of this study was to explore the reliability and clinical value of fast, accurate automatic segmentation of the aortic root based on a deep learning tool compared with computed tomography angiography. Methods: A deep learning tool for automatic 3-dimensional aortic root reconstruction, the CVPILOT system (TAVIMercy Data Technology Ltd., Nanjing, China), was trained and tested using computed tomography angiography scans collected from 183 patients undergoing transcatheter aortic valve replacement from January 2021 to December 2022. The quality of the reconstructed models was assessed using validation data sets and evaluated clinically by experts. Results: The segmentation of the ascending aorta and the left ventricle attained Dice similarity coefficients (DSC) of 0.9806/0.9711 and 0.9603/0.9643 for the training and validation sets, respectively. The leaflets had a DSC of 0.8049/0.7931, and the calcification had a DSC of 0.8814/0.8630. After 6 months of application, the system modeling time was reduced to 19.83 s. Conclusion: For patients undergoing transcatheter aortic valve replacement, the CVPILOT system facilitates clinical workflow. The reliable evaluation quality of the platform indicates broad clinical application prospects in the future.