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
Related News
•EurekAlert
EU Launches €20M CHAIN Project to Advance AI in Cardiovascular Care
The EU launches the CHAIN initiative to build a validated, federated AI ecosystem for cardiovascular health.
•EurekAlert
AI Model Combines ECG and Blood Tests to Spot Heart Transplant Rejection
An AI model integrating ECG data and blood biomarkers predicts heart transplant rejection with high specificity, reducing the need for invasive biopsies.
•EurekAlert
NIH Grant Funds AI Tool for Lung Cancer Discovery at Weill Cornell
Dr. Olivier Elemento receives an NIH Pioneer Award to develop an AI-human hybrid platform for cancer research, initially focusing on lung cancer using imaging and tissue data.