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

Area of Science:

• Medical Imaging
• Artificial Intelligence in Medicine
• Cardiology

Background:

• The left atrial appendage (LAA) is a key site for thrombus formation, often linked to pathological dilation and inflammation.
• Accurate identification and measurement of the LAA are crucial for cardiovascular risk assessment.
• Current imaging methods require precise LAA characterization.

Purpose of the Study:

• To evaluate the performance of four U-Net-based deep learning architectures for semiautomated LAA segmentation and volume measurement.
• To compare the accuracy and reliability of UNet3D, Residual-UNet3D, 3D Attention-UNet, and Res16-PAC-UNet.
• To determine the feasibility of using deep learning on noncontrast CT for LAA analysis.

Main Methods:

• Retrospective analysis of 452 noncontrast cardiac computed tomography (NCCT) scans from patients aged ≥60 years.
• Application of four U-Net deep learning models (UNet3D, Residual-UNet3D, 3D Attention-UNet, Res16-PAC-UNet) for LAA segmentation.
• Assessment of segmentation accuracy using Dice coefficients and volumetric agreement via Pearson correlation and Bland-Altman analysis.

Main Results:

• All four deep learning models demonstrated comparable segmentation accuracy, with Dice coefficients ranging from 77.68% to 79.07%.
• Strong correlations (P < 0.001) were observed between predicted and manual LAA volumes for all models, with 3D Attention-UNet showing the highest correlation (r = 0.800).
• Bland-Altman analysis confirmed minimal bias and narrow limits of agreement, indicating consistent reliability across all tested architectures.

Conclusions:

• Deep learning-based segmentation on NCCT allows for accurate and reproducible morphological and volumetric assessment of the LAA.
• This approach provides a rapid and reliable tool for cardiovascular risk stratification and treatment planning without the need for contrast agents.
• The study highlights the potential of AI in enhancing routine cardiac imaging analysis for improved patient care.