Machine Learning-Enabled Fully Automated Assessment of Left Ventricular Volume, Ejection Fraction and Strain: Experience in Pediatric and Young Adult Echocardiography

Background: Left ventricular (LV) volumes, ejection fraction (EF), and myocardial strain have been shown to be predictive of clinical and subclinical heart disease. Automation of LV functional assessment overcomes difficult technical challenges and complexities, potentially decreasing inter-observer and inter-center variability, reducing analysis times and improving echocardiography laboratory throughput and efficiency. We sought to assess whether a fully automated assessment of LV function could be reliably used in children and young adults. Methods: Fifty normal volunteers (22/28, female/male) were prospectively recruited for clinical research echocardiography. LV volumes, EF, and strain were measured both manually and automatically. An experienced sonographer performed all the manual analysis and recorded the analysis timing. The fully automated analyses were accomplished by 5 groups of observers with different knowledge and medical background (experienced sonographer, high school students, college students, medical students and pediatric cardiologists). AutoLV and AutoSTRAIN (TomTec) were employed for the fully automated LV analysis. The LV volumes, EF, strain, and analysis time were compared between manual and automated methods, and among the 5 groups of observers. Results: Software-determined endocardial border detection was achievable in all subjects. Image quality did not affect the ability of automated programs to record measurements. The analysis times of the experienced sonographer were significantly shorter for AutoLV than biplane Simpson’s method and AutoSTRAIN than manual strain analyses (p<0.001). Strong correlations were seen between conventional EF and AutoLV (r=0.8373), and between conventional three view global longitudinal strain (GLS) and AutoSTRAIN (r=0.9766). The volumes from AutoLV and three view GLS from AutoSTRAIN had strong correlations among different observers regardless of level of expertise. EF from AutoLV analysis had moderately strong correlations among different observers. Conclusion: Automated pediatric LV analysis is feasible in normal hearts. Machine learning-enabled image analysis saves time and produces results that are comparable to traditional methods.