Fully automated deep learning MAPSE: retrospective analysis and real-time clinical application.
Authors
Affiliations (5)
Affiliations (5)
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Box 8905 Trondheim 7491, Norway.
- Department of Cardiology and Thoracic Surgery, St.Olavs University Hospital, Trondheim, Norway.
- Department of Radiology and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway.
- Medical Image Analysis, Health Research, SINTEF Digital, Trondheim, Norway.
- Department of Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway.
Abstract
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. The DL-based method was evaluated in two retrospective cohorts (<i>n</i> = 1775) and one prospective cohort (<i>n</i> = 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 with -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 min 50 s) per examination. The DL method demonstrated excellent feasibility (96%). 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.