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Artificial Intelligence-Driven Screening System for Rapid Classification of 12-Lead ECG Exams: A Promising Solution for Emergency Room Prioritization
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  • Felipe Meneguitti Dias ,
  • Estela Ribeiro ,
  • ramon moreno ,
  • Adele Ribeiro ,
  • nelson samesima ,
  • carlos pastore ,
  • Jose Krieger ,
  • Marco Antonio Gutierrez
Felipe Meneguitti Dias
USP

Corresponding Author:[email protected]

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Estela Ribeiro
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ramon moreno
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Adele Ribeiro
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nelson samesima
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carlos pastore
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Jose Krieger
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Marco Antonio Gutierrez
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Abstract

The electrocardiogram (ECG) serves as a valuable diagnostic tool, providing crucial information about life-threatening cardiac conditions such as Atrial Fibrillation and Myocardial Infarction. A prompt and efficient assessment of ECG exams in environments like emergency rooms (ERs) can significantly improve the chances of survival for high-risk patients. In this study, we have developed an artificial intelligence-driven screening system specifically designed to analyze 12-lead ECG images. Our proposed method has been trained on an extensive dataset comprising 99,746 12-lead ECG exams collected from the ambulatory section of a tertiary hospital. The primary objective was to accurately classify the exams into three classes: Normal (N), Atrial Fibrillation (AFib), and Other (O). The evaluation of our method resulted in AUROC scores of 95.3%, 99.1%, and 93.3% for N, AFib, and O, respectively. To further validate our approach, we conducted evaluations using the Chinese Physiological Signal Challenge database. In this evaluation, we achieved AUROC scores of 91.8%, 97.5%, and 70.4% for the classes N, AFib, and O, respectively. Additionally, we assessed our method using 1,074 exams acquired in the ER, and achieved AUROC values of 98.3%, 98.0%, and 97.7% for the classes N, AFib, and O, respectively. Finally, we developed and deployed a system with a trained model within the ER of a tertiary hospital for research purposes. The system automatically retrieves newly captured ECG chart images from the Picture Archiving and Communication System (PACS) within the ER. These images undergo necessary preprocessing steps and serve as input for our proposed classification method. This comprehensive approach has resulted in the establishment of an efficient and versatile end-to-end framework for ECG classification. The results of our study highlight the potential of leveraging artificial intelligence in the screening of ECG exams, offering a promising solution for the rapid assessment and prioritization of patients in the ER.