DeepSTEMI: Artificial Intelligent Support System for Rapid Diagnosis and Treatment of ST-segment elevation Myocardial Infarction in Pre-hospital Emergency Medical Services at SRCA In Makkah Al-Mukarramah

Abstract

This research addresses the critical imperative for improved cardiac care in pre-hospital emergency services through the integration of an artificial intelligence (AI) based diagnostic system. A survey involving 237 participants in Saudi Arabia illuminates the essential need to minimize on-site duration during cardiac emergencies, with unanimous agreement among participants regarding the pivotal role of AI in expediting responses, also demographic analysis provides valuable insights into participant trends, contributing to a comprehensive background understanding. The study proposes a methodological pipeline that encompasses key elements, including data augmentation, ResNet50 model training, and the development of a user-friendly AI assistant named DeepSTEMI. This AI assistant is designed to predict specifically ST-segment elevation myocardial infarction (STEMI) from given images and respond to initial treatment. Demonstrating robust binary classification performance, the ResNet50 model consistently exhibits high precision, recall, F1-score, and accuracy. A validated area under the curve (AUC) score of 0.98 underscores the model's discriminative prowess in distinguishing STEMI from normal cases. Emphasizing practical strategies, the study advocates for collaboration with the Saudi Red Crescent Authority, continuous model refinement, and system expansion to address a broader spectrum of cardiac conditions. Furthermore, the research highlights the importance of integrating real-time data feeds and incorporating continuous learning as pivotal elements to enhance diagnostic precision.