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Abstract #0235

Explainable AI Enables Early Prediction of Intramyocardial Hemorrhage Risk in Acute Myocardial Infarction (MI) Patients

Khalid Youssef1,2, Keyur Vora1,2, Rajesh Gupta3, and Rohan Dharmakumar1,2
1Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States, 2Krannert Cardiovascular Research Center, Indiana University School of Medicine/IU Health Cardiovascular Institute, Indianapolis, IN, United States, 3Department of Medicine, Division of Cardiology, University of Toledo, Toledo, OH, United States

Synopsis

Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence, Intramyocardial Hemorrhage

Motivation: Hemorrhagic myocardial infarction (hMI) amplifies the mortality risk in MI patients. Currently, there no methods that can identify MI patients at risk for hMI.

Goal(s): To develop an explainable AI model to predict hMI.

Approach: Superposable Neural Network (SNN) was trained on data from 264 MI patients. T2* MRI was used to diagnose hMI and identify its key predictors.

Results: The model achieved an AUC of 0.92. A point-based scoring system, developed for predicting hMI, showed 84.9% accuracy, 82.3% sensitivity, and 87.3% specificity. The scoring system has the capacity to accurately identify hMI patients and open therapeutic opportunities to intervene to prevent/mitigate hMI.

Impact: By providing an accurate and interpretable method to predict hMI risk before reperfusion, this explainable-AI-based tool empowers clinicians to make informed, real-time decisions, potentially reducing complications and improving outcomes in patients mechanically revascularized for MI.

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