Fully automated deep learning MAPSE: retrospective analysis and real-time clinical application

Abstract Aims Mitral annular plane systolic excursion (MAPSE) is an accessible echocardiographic measure of left ventricular (LV) function. However, manual measurement methods are operator-dependent and time-consuming. We developed a multistep deep learning (DL) method for off-line and real-time fully automated MAPSE estimation, and aimed to assess agreement, reproducibility, time efficiency, and feasibility compared with standard manual measurements. Methods and results The DL-based method was evaluated in two retrospective cohorts (n = 1775) and one prospective cohort (n = 51). Agreement between DL-MAPSE on B-mode images and experts’ manual M-mode measurements was evaluated in all datasets. Evaluation of test-retest reproducibility, time efficiency using real-time analysis during acquisition, and agreement with cardiac magnetic resonance (CMR)-imaging were performed in subsets of the datasets. DL-MAPSE demonstrated good agreement with manual measurements, with bias 2.9 mm (95% CI 2.8-3.0 mm) and Pearson coefficient 0.81 (95% CI 0.79 - 0.84) in the primary dataset, and a lower bias of 1.0 mm against CMR-MAPSE compared to -2.1 mm using manual M-mode. Both DL and manual measurements showed good test–retest reproducibility (ICC 0.82 and 0.76, respectively). Real-time DL measurements reduced measurement and acquisition time by 51% (mean 1 minute 50 seconds) per examination. The DL method demonstrated excellent feasibility (96%). Conclusion This novel DL method for fully automated MAPSE demonstrated excellent feasibility, robust reproducibility, and good agreement with both manual M-mode and CMR-derived measurements. Automated DL-MAPSE could substantially reduce analysis time and enhance reproducibility, increasing its clinical value as a marker of LV systolic function.