A deep learning-based pipeline for large-scale echocardiography data curation and measurements

Background: Echocardiographic image data accumulating in echo labs are a highly valuable but underutilized resource for cardiac imaging research. Despite the availability of large image databases, quantitative measurements required for clinical analysis and research remain limited. Retrospective manual measurements are highly time-consuming and susceptible to operator-related variability. Moreover, data curation and quality control metrics are needed to prepare real-world data for analysis. Methods: Deep learning-based image analysis can provide fully automated, rapid, and consistent extraction of measurements, given that the data have been properly curated. In this work, we develop an automated pipeline for data curation of a large echo database of 14 326 exams from 9678 patients and evaluate automated measurements of left ventricular ejection fraction (LVEF) and left atrial volume index (LAVI) as a use case. Results: In validation subsample of 1763 subjects with varying image quality and cardiac diseases and 1488 healthy subjects, the pipeline output was compared with manual measurements. Bland-Altman analysis revealed a bias standard deviation (SD) of -1.8% (7.6%) for LVEF and 3.3 mL/m² (8.1 mL/m²) for LAVI and demonstrated robust performance for varying image quality and pathological conditions. Additionally, in the large part of the database of 9678 exams without clinical measurements, the automated data curation and measurement quality control resulted in 79% measured data with high confidence. Conclusion: This work highlights the potential of deep learning-based automated measurements in echocardiography for data mining in large real-world databases, paving the way for advancements in cardiac imaging research and diagnostics.