Deep learning in echocardiography: Fully automated B-mode MAPSE measurements in real-time
Authors: Holmstrom V , Smistad E , Grue J F , Lovstakken L , Stolen S , Dalen H , Ostvik A , Holte E , Grenne B 
Published: January 2025

Abstract

Background
Accurate quantification of left ventricular (LV) systolic function is crucial in echocardiography. LV Ejection Fraction (LV EF) and LV Global Longitudinal Strain (LV GLS), rely on clear delineation of the endocardial border, limiting their utility in patients with suboptimal acoustic windows. In contrast, Mitral Annular Plane Systolic Excursion (MAPSE) requires only the visualization of the mitral annulus with its strong acoustic reflections, offering near-perfect feasibility. However, conventional M-mode MAPSE is angle-dependent and prone to errors due to out-of-line movement of the mitral annulus.

Purpose
To validate and test a novel optical-flow-based deep learning (DL) application for fully automated B-mode MAPSE measurements in real-time during scanning (DL-MAPSE), assessing its agreement with conventional M-mode MAPSE, and correlation with LV GLS and LV EF.

Methods
Deep learning networks were trained to recognize apical views, determine the timing of end-systole and end-diastole, identify landmarks, estimate motion, and track B-mode MAPSE in real-time (Figure 1). This approach was tested in a prospective cohort of 52 patients. M-mode MAPSE and 2DS LV GLS measurements were obtained using semi-automatic software (EchoPAC version 204, GE Healthcare). Apical 4-chamber septal and lateral MAPSE values were acquired and averaged with both methods. Agreement and correlation were assessed using Bland-Altman plots and Pearson correlation coefficients.

Results
The feasibility for DL-MAPSE was 98%. We observed a method-specific bias, with lower MAPSE values for DL-MAPSE in the apical 4-chamber view (bias -2.95 mm, LoA -7.56 to 1.66, Figure 2A). There was a moderate correlation between methods (r = 0.60, 95% CI 0.40–0.75). DL-MAPSE and GLS showed a moderate-to-strong correlation (r = 0.69, 95% CI 0.51–0.81, figure 2B), which was stronger than that between GLS and M-mode MAPSE (r = 0.57, 95% CI 0.34–0.73). However, the difference in correlation strength was not statistically significant after Fishers’s Z-test (p = 0.33). MAPSE and LV EF had weak-to-moderate correlations (DL-MAPSE: r = 0.38, M-Mode MAPSE: r = 0.42, p = 0.80).
For a subgroup of patients, acquisition and measurement times for DL-MAPSE and M-mode MAPSE were compared. M-mode image acquisition (septal and lateral-focused) took an average of 36 ± 10 seconds, with analysis requiring 37 ± 8 seconds, totaling a mean of 73 ± 13 seconds to acquire M-mode MAPSE values. In contrast, the acquisition and analysis time of DL-MAPSE was 33 ± 15 seconds, resulting in a mean time reduction of 40 seconds (95% CI 32-48 seconds).

Conclusion
Fully automated DL-based B-mode MAPSE measurements in real-time are feasible, efficient, and with strong correlation to LV GLS. By addressing the potential pitfalls of incorrect angling and out-of-line movement, this method offers the potential for more reliable MAPSE measurements, enhancing its utility in clinical practice.

Agreement and correlation