AI could make heart attack risk detection safer and smarter

NCT ID NCT04193475

Recruiting now Knowledge-focused Sponsor: Hull University Teaching Hospitals NHS Trust Source: ClinicalTrials.gov ↗

First seen Jun 27, 2026 · Last updated Jun 27, 2026

Summary

This study aims to improve how doctors interpret stress echocardiograms—ultrasounds of the heart during exercise—by using machine learning. Researchers will analyze data from 1,250 patients to develop more accurate tools for detecting blocked arteries and heart attack risk. The goal is to reduce the need for invasive follow-up tests and help doctors choose the best treatment for each patient.

What this could mean

Our plain-language read of the trial. This is informational only — not medical advice or a prediction.

What this could lead to: If successful, this could make stress echocardiograms more accurate, helping doctors identify heart disease risk earlier and reducing the need for invasive procedures.
What could go wrong: This is an observational study developing new analysis methods, not testing a treatment. The machine learning tools may not prove reliable enough for routine clinical use.

This is an AI summary of the original study and may miss details. Read our disclaimer.

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Conditions

The condition(s) this trial relates to.

Angina Pectoris cardiovascular disorder Chest Pain coronary artery disorder myocardial ischemia Sprains and Strains

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

CARDIOVASCULAR DISEASES ISCHAEMIC HEART DISEASE

Contacts and locations

Locations

Castle Hill Hospital
RECRUITING

Cottingham, HU165JQ, United Kingdom

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