Real-Time Global Longitudinal Strain During Echocardiography: A Deep Learning Platform for Improved Workflow.

Background: Left ventricular (LV) global longitudinal strain (GLS) offers advantages over LV ejection fraction, including improved diagnostic sensitivity, reproducibility, and prognostic value. However, current semi-automatic analyses are time-consuming and operator-dependent, impeding widespread adoption of GLS in routine clinical practice.

Objectives: We aimed to assess the feasibility, precision, and time-efficiency of GLS measurements using a deep learning (DL) platform that performs real-time GLS analysis during image acquisition and incorporates DL tools to support standardization, to evaluate whether DL-assisted acquisitions can enhance image quality metrics relevant to strain analyses.

Methods: A DL platform was developed for fully automated real-time GLS analysis, including tools that detect and alert the operator to foreshortening or baseline drift. In this controlled prospective study, 50 patients (mean age 56 years, 64% males) were included. Two image sets were acquired by different operators using the DL platform and a conventional workflow, and GLS and image quality were compared.

Results: Overall feasibility of DL-based GLS measurements was 94%. Absolute GLS was 14.8 ± 3.2 using the DL platform workflow and 16.2 ± 3.3 with manual reference measurements, with a bias of -1.3 and limits of agreement ranging from -3.5 to 0.8. Correlation was excellent (r = 0.94). Images acquired with the DL platform showed significantly less baseline drift and borderline improved territorial strain agreement than the reference acquisition. The median time obtaining GLS with the DL platform was reduced by 57% compared to the conventional workflow, from 4 minutes and 48 seconds to 2 minutes and 4 seconds.

Conclusion: The DL platform for fully automated real-time GLS measurements was feasible, precise, and time efficient. Real-time DL-based feedback allows operators to optimize images during acquisition, thus improving quality metrics relevant to GLS analyses. Implementing this method in clinical practice could streamline workflow and improve efficiency in the echocardiographic laboratory.