Predicting Intramyocardial Hemorrhage Before Reperfusion in STEMI Patients With Intrinsically Explainable Artificial Intelligence.

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Bibliographic Details
Title: Predicting Intramyocardial Hemorrhage Before Reperfusion in STEMI Patients With Intrinsically Explainable Artificial Intelligence.
Authors: Youssef K; Cardiovascular Imaging Research Center, Medical Imaging Research Institute, Indiana University School of Medicine, Indianapolis, Indiana, USA; Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA., Vora KP; Cardiovascular Imaging Research Center, Medical Imaging Research Institute, Indiana University School of Medicine, Indianapolis, Indiana, USA; Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA., Gupta R; Division of Cardiology, Department of Medicine, University of Toledo, Toledo, Ohio, USA., Gruionu G; Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA., Kumar A; Division of Cardiology, Department of Medicine, Northern Ontario School of Medicine University, Sudbury, Ontario, Canada., Puri R; Division of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA., Reed GW; Division of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA., Kalra A; Division of Cardiology, Department of Medicine, State University of New York (SUNY), Upstate Medical University, Syracuse, New York, USA., Dharmakumar R; Cardiovascular Imaging Research Center, Medical Imaging Research Institute, Indiana University School of Medicine, Indianapolis, Indiana, USA; Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA. Electronic address: rdkumar@iu.edu.
Source: JACC. Advances [JACC Adv] 2026 Jun 11; Vol. 5 (7), pp. 102864. Date of Electronic Publication: 2026 Jun 11.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Inc Country of Publication: United States NLM ID: 9918419284106676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2772-963X (Electronic) Linking ISSN: 2772963X NLM ISO Abbreviation: JACC Adv
Database: MEDLINE Ultimate
Description
ISSN:2772-963X
DOI:10.1016/j.jacadv.2026.102864