Development and validation of a deep learning-based fully automated algorithm for pre-TAVR CT assessment of the aortic valvular complex and detection of anatomical risk factors: a retrospective, multicentre study.

ABSTRACT:

Background

Pre-procedural computed tomography (CT) imaging assessment of the aortic valvular complex (AVC) is essential for the success of transcatheter aortic valve replacement (TAVR). However, pre-TAVR assessment is a time-intensive process, and the visual assessment of anatomical structures at the AVC shows interobserver variability. This study aimed to develop and validate a deep learning-based algorithm for pre-TAVR CT assessment and anatomical risk factor detection.

Methods

This retrospective, multicentre study used AVC CT scans to develop a deep learning-based, fully automated algorithm, which was then internally and externally validated. After loading CT scans into the algorithm, it automatically assessed the essential anatomical structure data required for TAVR plan

SUBMITTER: Wang M 

PROVIDER: S-EPMC10507199 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature