Deep learning models for segmentation and quantification of left atrial appendage volume using noncontrast cardiac computed tomography.

ABSTRACT:

Background

The left atrial appendage (LAA) is a critical but frequently overlooked site of thrombus formation, reinforcing the need for accurate identification in routine cardiac imaging. This process is related to pathological dilation associated with endothelial injury and a proinflammatory status. This study assesses the performance of deep learning architectures based on U-Net, specifically UNet3D, Residual-UNet3D, 3D Attention-UNet, and Res16-PAC-UNet, in the semiautomated segmentation and volume measurement of LAA.

Methods

We retrospectively analyzed noncontrast cardiac computed tomography (NCCT) scans from 452 patients aged ≥ 60 years, acquired for chest pain evaluation, to compare the performance of four U-Net-based deep learning architectures (UNet3D, Residual-UNet3

SUBMITTER: Santos DAM 

PROVIDER: S-EPMC12579425 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature