Researchers at Indiana University School of Medicine have developed and tested a six-point scoring system using explainable artificial intelligence (XAI) to identify patients at high risk of intramyocardial hemorrhage (IMH), a life-threatening complication of a heart attack that can occur after doctors restore blood flow through a blocked artery. IMH affects about 40% of patients treated for ST-segment elevation myocardial infarction (STEMI) and increases the risk of heart failure and death. The scoring system is designed for interventional cardiologists to use in cardiac catheterization labs before reopening a patient’s blocked artery. The study, published in JACC: Advances, found the system could accurately predict IMH using clinical information already available during cardiac catheterization. Researchers applied a traceable AI tool called Superposable Neural Networks (SNN) and converted the model into a six-point score, with a score of 4 or higher classified as high risk and 3 or lower as low risk. The score uses three measurements from an electrocardiogram and angiography. Lead author Khalid Youssef, PhD, noted that XAI shows the reasoning behind the prediction without needing to wait for a cardiac MRI. Currently, a specialized cardiac MRI scan known as T2* is the standard method for detecting IMH, but it is often performed 48 to 72 hours after the blocked artery has been opened. The new scoring system estimates risk before blood flow is restored and is not intended to delay treatment or replace a physician’s judgment