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

Background: Accurate and reproducible quantification of LA anatomy from CTA is essential for ablation, LAAC, and structural interventions, yet manual measurements are time-consuming and prone to inter-observer variability. Objectives: To validate a deep learning-based CTA pipeline for automated quantification of eight clinically relevant LA metrics, and to assess its agreement, repeatability, and efficiency compared with expert measurements. Methods: In this retrospective study, 407 patients were included and divided into training (n = 270), validation (n = 87), and clinical evaluation (n = 50) cohorts. A MedNeXt-based model performed multi-structure segmentation, and a geometry-driven framework computed eight metrics: LA volume, LAA volume, AP/ML/SI diameters, left/right PV ostial size, left/right PV inter-ostial angles, and LAA ostial size. Automated outputs were compared with expert annotations using intraclass correlation coefficients (ICCs) and Bland-Altman analysis (primary endpoints: LA volume and diameters). Workflow efficiency and usability (Likert scale, 1-5) were also assessed by electrophysiology/structural experts. Results: Automated measurements demonstrated excellent agreement with experts for primary endpoints (LA volume ICC = 0.999; AP/ML/SI diameter ICCs = 0.972-0.985), with minimal bias and narrow limits of agreement. Agreement for other metrics was good to excellent (typical ICCs ≥0.84). Analysis time was reduced from 15.3 ± 1.4 min to 0.5 ± 0.1 min per case (≈92% reduction; p < 0.001). Usability ratings were ≥4/5 in most cases, with 63%-76% classified as Grade A (fully usable without manual edits). Performance remained consistent across voxel-size strata. Conclusion: The proposed pipeline enables rapid, reproducible, and expert-level quantification of eight LA metrics on CTA, demonstrating technical feasibility for clinical integration pending prospective validation of procedural impact.