Clinical Validation of an Artificial Intelligence-assisted Algorithm for Automated Quantification of Left Ventricular Ejection Fraction in Real Time by a Novel Handheld Ultrasound Device

Aims: We sought to evaluate the reliability and diagnostic accuracy of a novel handheld ultrasound device (HUD) with artificial intelligence (AI) assisted algorithm to automatically calculate ejection fraction (autoEF) in a real-world patient population.

Methods And Results: We studied 100 consecutive patients (57 ± 15 years old, 61% male), including 38 with abnormal left ventricular (LV) function [LV ejection fraction (LVEF) < 50%]. The autoEF results acquired using the HUD were independently compared with manually traced biplane Simpson's rule measurements on cart-based systems to assess method agreement using intra-class correlation coefficient (ICC), linear regression analysis, and Bland-Altman analysis. The diagnostic accuracy for the detection of LVEF <50% was also calculated. Test-retest reliability of measured EF by the HUD was assessed by calculating the ICC and the minimal detectable change (MDC). The ICC, linear regression analysis, and Bland-Altman analysis revealed good agreement between autoEF and reference manual EF (ICC = 0.85;  = 0.87,  < 0.001; mean bias -1.42% with limits of agreement 14.5%, respectively). Detection of abnormal LV function (EF < 50%) by autoEF algorithm was feasible with sensitivity 90% (95% CI 75-97%), specificity 87% (95% CI 76-94%), PPV 81% (95% CI 66-91%), NPV 93% (95% CI 83-98%), and a total diagnostic accuracy of 88%. Test-retest reliability was excellent (ICC = 0.91,  < 0.001;  = 0.91,  < 0.001; mean difference ± SD: 0.54% ± 5.27%,  = 0.308) and MDC for LVEF measurement by autoEF was calculated at 4.38%.

Conclusion: Use of a novel HUD with AI-enabled capabilities provided similar LVEF results with those derived by manual biplane Simpson's method on cart-based systems and shows clinical potential.