AI-powered ultrasound analysis identifies high-risk heart failure cases
Researchers at Weill Cornell Medicine and other institutions have developed an AI-powered method to identify patients with advanced heart failure using cardiac ultrasound data and electronic health records. This new method may improve care for thousands of patients who are currently overlooked due to diagnostic challenges.
A new study has found that applying artificial intelligence to cardiac ultrasound data can help identify patients with advanced heart failure. The method uses ultrasound images and electronic health records to predict peak oxygen consumption, a key measure of heart function. This approach may remove the current diagnostic bottleneck, which relies on specialized equipment and staff. The study was conducted by researchers at Weill Cornell Medicine, Cornell Tech, and other institutions. They developed a machine learning model that can process multiple data types, including ultrasound images and waveform imagery. The model was tested and found to predict peak oxygen consumption with high accuracy. This breakthrough may lead to better care for patients with advanced heart failure.
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