Mayo Clinic researchers have developed an AI model that detects heart obstructions from routine ultrasound, potentially improving early identification of at-risk patients.
Key Details
- 1The AI model identifies left ventricular outflow tract (LVOT) obstruction in hypertrophic cardiomyopathy (HCM) using non-Doppler ultrasound videos.
- 2External validation was performed with 46 patients from a South Korean hospital, showing strong model performance despite population differences.
- 3Combining three standard ultrasound views improved model accuracy for detecting elevated LVOT gradients.
- 4In some cases, the AI outperformed two expert echocardiographers using only standard images.
- 5The technology intends to complement Doppler echocardiography, aiding early detection and triage.
- 6No external funding was received for the study; results are published in Circulation: Cardiovascular Imaging.
Why It Matters
This AI advancement may enable more accessible and timely identification of significant cardiac obstructions, especially where Doppler expertise is lacking. It demonstrates how imaging AI could extend cardiac screening capabilities in varied clinical settings.
Source
EurekAlert
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