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; r = 0.87, P < 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, P < 0.001; r = 0.91, P < 0.001; mean difference ± SD: 0.54% ± 5.27%, P = 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.