Abstract Background Accurate measurements of left ventricular (LV) wall thickness and chamber dimensions in the parasternal long-axis view (PLAX) are essential in the standard echocardiographic examination. Manual assessments introduce variability, and conducting the numerous measurements required for a comprehensive echocardiographic assessment is time-consuming. Therefore, there is potential for improving workflow. Purpose To develop a novel deep learning (DL)-based method for fully automated, real-time measurements of LV wall thickness and chamber dimensions while scanning the patient, and to validate its performance in an unselected prospective cohort. Methods PLAX recordings from 207 subjects were annotated to train DL networks for cardiac segmentation, timing of end-systole and end-diastole, measurements of LV wall thickness and chamber dimensions. The application presents results from three consecutive cardiac cycles in real-time to the operator for review (Figure 1). Agreement and correlation with reference measurements were evaluated, and the combined acquisition and measurement times for both the novel- and reference methods were recorded. Results Feasibility for real-time measurements in PLAX was 98%, with one patient excluded due to poor acoustic window. Agreement with reference measurements was good for interventricular septal diameter (IVSd) (bias - 0.7 mm, limits of agreement (LoA) -4.38 to 2.94 mm), posterior wall thickness (PWd) (bias 0.8 mm, LoA -2.32 to 3.84 mm) and left ventricular internal diastolic diameter (LVIDd) (bias 0.0 mm, LoA -7.0 to 6.9 mm, Figure 2A). Strong correlations were found for IVSd (R=0.84), PWd (R=0.66) and LVIDd (R=0.85), all significant (p <0.001). The average time for acquiring PLAX images was 53 ± 22 seconds, with an additional 42 ± 11 seconds for reference analysis, totaling 94 ± 27 seconds to complete reference PLAX measurements. In contrast, the real-time application took only 59 ± 34 seconds to obtain the same measurements (Figure 2B), resulting in a mean time reduction of 36 seconds (95% CI 24-47 seconds). Conclusion This novel DL-based method for fully automated, real-time analysis of key metrics in PLAX is time-efficient, feasible, and yields measurements in good agreement with manual reference. This method has the potential to substantially improve efficiency and precision of PLAX assessment in clinical practice.
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