What is coronary microvascular dysfunction (CMVD) and why is it hard to diagnose?

CMVD affects the heart's small blood vessels, causing chest pain and increasing heart attack risks. It's hard to diagnose because standard tests like angiograms often don't detect damage to these tiny vessels.

How does the new EKG-AI model help diagnose CMVD?

The EKG-AI model was trained to analyze the electrical signals of the heart, enabling clinicians to accurately identify CMVD. This offers a more convenient, cost-effective, and noninvasive diagnostic approach.

Who will benefit most from the EKG-AI model for CMVD diagnosis?

Hospitals with limited resources and non-specialty centers will greatly benefit from this AI tool, as it provides an accessible and effective way to diagnose CMVD and assess myocardial flow reserve.

Home / Health / AI Spots Hard-to-Diagnose Heart Condition

AI Spots Hard-to-Diagnose Heart Condition

18 Dec, 2025

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Summary

New AI model accurately identifies coronary microvascular dysfunction (CMVD).
CMVD is notoriously difficult to diagnose, often missed in emergency visits.
EKG-AI offers a cost-effective, noninvasive tool for early detection.

AI Spots Hard-to-Diagnose Heart Condition

A new artificial intelligence model is poised to significantly improve the detection of coronary microvascular dysfunction (CMVD), a heart condition that affects the tinier blood vessels supplying the heart and is notoriously difficult to diagnose using standard methods like angiograms.

Developed by researchers, this AI model has been trained to "understand the electrical language of the heart" and can now assist clinicians in accurately identifying CMVD, which often presents as chest pain but can be missed during emergency department visits. This advancement offers hope for earlier intervention and better patient outcomes.

The EKG-AI model is anticipated to be particularly beneficial for hospitals with limited resources, providing an easy, cost-effective, and noninvasive way to predict myocardial flow reserve and CMVD. This technology aims to overcome the limitations of traditional diagnostic tools, making CMVD detection more accessible and efficient.