Explainable AI score predicts heart muscle bleeding risk before artery reopening

by Jean Albanese, State University of New York Upstate Medical University

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Upstate Medical University cardiologist Ankur Kalra helped lead a team of researchers that developed and tested a scoring system to help identify patients at high risk of bleeding into damaged heart muscle after a severe heart attack using explainable artificial intelligence (XAI).

Kalra, MD, MSc, FACP, FACC, FSCAI, director of cardiac catheterization laboratories and chief of cardiology at Upstate, is the co-principal investigator on the National Institutes of Health/National Heart, Lung, and Blood Institute grant. The study, "Predicting Intramyocardial Hemorrhage Before Reperfusion in STEMI Patients With Intrinsically Explainable Artificial Intelligence," was recently published in the JACC: Advances.

"These findings are significant, as appropriate identification will lead to incremental therapy with intravenous dexrazoxane," said Kalra, a structural heart interventional cardiologist. "Dexrazoxane is an investigational drug; we are presenting our findings from a phase 2a study in Munich on Aug. 31 at the European Society of Cardiology Congress. We anticipate the manuscript will be simultaneously published in one of the highest-impact-factor journals in cardiology."

A dangerous complication after treatment

Myocardial infarction (MI)—commonly known as a heart attack—is a significant global health concern, with more than 800,000 cases reported annually in the United States alone.

Intramyocardial hemorrhage, or IMH, is a life-threatening complication of a heart attack that can occur after doctors restore blood flow through a blocked artery. IMH, the most serious form of heart muscle injury, affects about 40% of patients treated for ST-segment elevation myocardial infarction, or STEMI, a severe type of heart attack, and increases the risk of heart failure and death.

Built for decisions in the cath lab

The new scoring system is designed for interventional cardiologists to use in cardiac catheterization, or CATH, labs before reopening a patient's blocked artery.

The score could eventually help clinicians assess risk in real time during emergency angiography, when providers check for blockages in the heart's arteries, or help identify patients who need closer monitoring after a blocked artery is reopened. The score may also help doctors decide which patients need a cardiac MRI and who may qualify to participate in a clinical trial aimed at reducing damage from IMH.

"This is the kind of AI we need for accurate, interpretable and usable data at the point of care," Kalra said. "The SNN method allows interventionalists to see exactly which factors are driving the prediction, rather than being asked to trust a black box."

Kalra added that, at some point, Upstate will have this type of XAI.

"These findings are actionable in an electronic health record-based calculator that can help identify these patients," he said.

Collaborators on the study included the Indiana University School of Medicine; the University of Toledo College of Medicine and Life Sciences; Northern Ontario School of Medicine University; Cleveland Clinic; and Upstate Medical University.

More information

Khalid Youssef et al, Predicting Intramyocardial Hemorrhage Before Reperfusion in STEMI Patients With Intrinsically Explainable Artificial Intelligence, JACC: Advances (2026). DOI: 10.1016/j.jacadv.2026.102864

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