Key result
AI-assisted handheld ultrasound shows good agreement with manual EF measurements.
Why the study?
The reliability and diagnostic accuracy of a novel handheld ultrasound device using an AI-assisted algorithm to automatically calculate ejection fraction in real-world patients required evaluation.
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?
Population
100 consecutive patients
Comparison
Handheld ultrasound device autoEF vs manual biplane Simpson rule measurements on cart-based systems
Design
Validation study
Authors
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May support point-of-care LVEF screening; hypothesis-generating and requires prospective validation.
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?
Effect estimate: ICC 0.85
p-value: p=<0.001
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.
Papadopoulou et al. (2022) conducted an 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).
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