A novel handheld ultrasound device with an AI-assisted algorithm for automated ejection fraction calculation showed good agreement with manual measurements (ICC 0.85, P<0.001).
Observational (n=100)
Does an AI-assisted algorithm on a handheld ultrasound device accurately and reliably calculate left ventricular ejection fraction compared to manual measurements on cart-based systems?
An AI-assisted algorithm on a handheld ultrasound device provides reliable and accurate automated LVEF measurements comparable to manual biplane Simpson's method on standard cart-based systems.
Effect estimate: ICC 0.85
p-value: p=<0.001
Abstract 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.
Papadopoulou et al. (Thu,) conducted a observational in Real-world patient population (n=100). Handheld ultrasound device (HUD) with AI-assisted algorithm (autoEF) vs. Manually traced biplane Simpson's rule measurements on cart-based systems was evaluated on Agreement between autoEF and reference manual EF (ICC 0.85, p=<0.001). A novel handheld ultrasound device with an AI-assisted algorithm for automated ejection fraction calculation showed good agreement with manual measurements (ICC 0.85, P<0.001).