The role of artificial intelligence in standardizing global longitudinal strain measurements in echocardiography

To evaluate the accuracy and feasibility of artificial intelligence (AI) in left ventricular global longitudinal strain (GLS) analysis as compared to conventional (Manual) and semi-automated (SemiAuto) method in echocardiography (Echo).

GLS validation was performed on 550 standard Echo exams by expert cardiologists. The performance of a beginner cardiologist without experience of GLS analysis was assessed on a subset of 90 exams. The AI employs fully automated view selection, classification, endocardial border tracing, and calculation of GLS from an entire Echo exam, while SemiAuto requires manual chamber view selection, and Manual involves full user input. Interobserver agreement was assessed using the intraclass correlation coefficient (ICC) for all three methods. Agreement of measures included Pearson's correlation (R) and Bland-Altman analysis [median bias; limits of agreement (LOA)]. With an 89% feasibility the AI showed good agreement with Manual (R = 0.92, bias = 0.7% and LOA: -3.5 to 4.8%) and with SemiAuto (r = 0.90, bias = 0.10% and LOA: -4.5 to 4%). ICCs for GLS were 1.0 for AI, 0.93 for SemiAuto, and 0.80 for Manual. After the 55th analysis, the beginner showed stable time performance with Manual (171 s), contrasting with the consistent performance of SemiAuto (85-69 s) from the beginning. The highest agreement between beginner and expert readers was achieved with AI (R = 1.00), followed by SemiAuto (R = 0.85) and Manual (R = 0.74).

Automated GLS analysis enhances efficiency and accuracy in cardiac diagnostics, particularly for novice users. Integration of automated solutions into routine clinical practice could yield more standardized results.