Korean Circulation Journal

The Korean Society of Cardiology (대한심장학회)
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An Artificial Intelligence-Based Automated Echocardiographic Analysis: Enhancing Efficiency and Prognostic Evaluation in Patients With Revascularized STEMI

  • Yeonggul Jang (CONNECT-AI Research Center, Yonsei University College of Medicine) ;
  • Hyejung Choi (Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital) ;
  • Yeonyee E. Yoon (Ontact Health Inc.) ;
  • Jaeik Jeon (CONNECT-AI Research Center, Yonsei University College of Medicine) ;
  • Hyejin Kim (Ontact Health Inc.) ;
  • Jiyeon Kim (CONNECT-AI Research Center, Yonsei University College of Medicine) ;
  • Dawun Jeong (CONNECT-AI Research Center, Yonsei University College of Medicine) ;
  • Seongmin Ha (CONNECT-AI Research Center, Yonsei University College of Medicine) ;
  • Youngtaek Hong (CONNECT-AI Research Center, Yonsei University College of Medicine) ;
  • Seung-Ah Lee (CONNECT-AI Research Center, Yonsei University College of Medicine) ;
  • Jiesuck Park (Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital) ;
  • Wonsuk Cho (Department of Internal Medicine, Seoul National University College of Medicine) ;
  • Hong-Mi Choi (Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital) ;
  • In-Chang Hwang (Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital) ;
  • Goo-Yeong Cho (Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital) ;
  • Hyuk-Jae Chang (CONNECT-AI Research Center, Yonsei University College of Medicine)
  • Received : 2024.02.06
  • Accepted : 2024.08.14
  • Published : 2024.11.01
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Abstract

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81-0.92 and intraclass correlation coefficients ranging 0.74-0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions: Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Keywords

Acknowledgement

This work was supported by a grant from the Institute of Information & Communications Technology Planning & Evaluation (IITP), funded by the Korea government (MSIT) (No. 2022000972, Development of flexible mobile healthcare software platform using 5G MEC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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