AI software may replace invasive heart tests — a validation study puts it to the test
NCT ID NCT07763769
First seen Aug 13, 2026 · Last updated Aug 14, 2026 · Updated 1 time
Summary
This study tests whether a software tool called AutocathFFR can accurately measure the severity of coronary artery blockages from standard 2D angiogram images, compared to the gold-standard invasive FFR measurement. The software uses machine learning to analyze images taken during cardiac catheterization, potentially avoiding the need for a pressure wire. Researchers will evaluate its sensitivity and specificity in adults with stable or unstable angina or NSTEMI who underwent both procedures. If the software proves accurate, it could offer a less invasive way to guide treatment decisions.
What this could mean
Our plain-language read of the trial. This is informational only — not medical advice or a prediction.
- Active substance
- MedHub AutocathFFR, a software device that analyzes standard 2D coronary angiogram images using machine learning to estimate fractional flow reserve (FFR)
- What this could lead to
- If validated, this software could let doctors assess coronary artery disease severity without invasive pressure-wire measurements, making diagnosis faster and less risky.
- What could go wrong
- This is an observational validation study, not a treatment trial. The software's accuracy may not meet the performance goals, and results may not generalize beyond the studied population.
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.
As listed by the trial registrant
The condition terms exactly as the trial's registrant entered them.
Contacts and locations
Locations
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Emory University Hospital
Atlanta, Georgia, 30322, United States
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MedStar Washington Hospital Center
Washington D.C., District of Columbia, 20010, United States
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Northeast Georgia Medical Center
Gainesville, Georgia, 30501, United States
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University of Virginia Health System
Charlottesville, Virginia, 22908, United States
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