Automated three-dimensional left atrial analysis on computed tomography angiography: reproducibility and workflow efficiency of eight clinically relevant metrics using a deep learning pipeline

Figures

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  FIGURE 1. Flow chart. A workflow diagram showing 421 enrolled scans, with 14 excluded due to poor image quality. The remaining 407 scans are divided into training (270), validation (87), and evaluation (50) sets. These sets are utilized to train and evaluate a UNet model for measurements of CT segmented data, including volume, diameter, pulmonary veins, and the left atrial appendage.

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  FIGURE 2. Performance of the MedNeXt-based network for multi-structure cardiac segmentation. (A) Dice coefficient curves during training and validation for the aorta, LA, PV sleeves, and LAA, demonstrating rapid convergence and stable high accuracy. (B) Comparison of training versus validation sets for surface Dice coefficient (SDC, left) and 95th percentile Hausdorff distance (HD95, right), confirming consistent generalization across datasets with minimal performance drop between training and validation cohorts.

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  FIGURE 3. Comparison of time requirements for manual versus automated quantification across the validation set (left) and clinical evaluation set (right). Automated analysis reduced per-case time from a median of approximately 15 min to less than 1 min, yielding a >90% relative reduction (p < 0.001 for both cohorts).

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  FIGURE 4. Representative Bland–Altman plots showing agreement between automated and expert-derived measurements for selected metrics from the predefined eight-category framework. Illustrated indices include LA volume, LA diameters (AP, ML, SI), left superior PV ostial equivalent diameter, left PV inter-ostial angle, LAA volume, and LAA ostial equivalent diameter. Plots demonstrate minimal systematic bias and narrow limits of agreement, confirming excellent reproducibility of automated analysis.

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  FIGURE 5. Representative examples of automated grading outcomes and failure modes. (A, B) Grade A: case demonstrating accurate and complete metric extraction, including consistent LA diameters, PV ostial planes, and LAA segmentation. (C, D) Grade B: examples requiring minor manual adjustment. Panel C illustrates a case with aberrant LAA segmentation, while Panel D shows a case with a PV common trunk resulting in abnormal ostial-plane placement. (E, F) Grade C: case with major errors, in which both LAA and PV segmentations were inaccurate, precluding reliable metric generation.