An artificial intelligence-based platform for automatically estimating time-averaged wall shear stress in the ascending aorta.

ABSTRACT:

Aims

Aortopathies are a series of disorders requiring multiple indicators to assess risk. Time-averaged wall shear stress (TAWSS) is currently considered as the primary indicator of aortopathies progression, which can only be calculated by Computational Fluid Dynamics (CFD). However, CFD's complexity and high computational cost, greatly limit its application. The study aimed to construct a deep learning platform which could accurately estimate TAWSS in ascending aorta.

Methods and results

A total of 154 patients who had thoracic computed tomography angiography were included and randomly divided into two parts: training set (90%, n = 139) and testing set (10%, n = 15). TAWSS were calculated via CFD. The artificial intelligence (AI)-based model was trained and as

SUBMITTER: Lv L 

PROVIDER: S-EPMC9779925 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature