Artificial Intelligence Powered Real-Time Coronary Stenosis Recognition and Quantification in Angiography.

Cardiovascular Critical Care Dermatology Emergency Medicine Geriatrics
Journal: Journal of imaging informatics in medicine
Published Date: Jun 24, 2025

Abstract

Effective and automated measurement of coronary lesions is essential for timely decision-making during interventions. However, a comprehensive, real-time strategy remains limited. This study aimed to develop a real-time deep learning system for automated detection and quantification of stenotic lesions in coronary angiography. The model was trained using 2651 diagnostic coronary angiographic images from 502 adult patients collected between February 2015 and January 2022 at two tertiary care hospitals. The system integrates five core components: vessel type classification, keyframe selection, lesion detection, vessel segmentation, and quantitative coronary angiography (QCA). In internal and external datasets, vessel type classification accuracies reached 96.33% and 94.19%, while keyframe selection accuracies were 98.29% and 93.27%, respectively. Lesion detection achieved recall/precision scores of 0.93/0.89 internally and 0.92/0.76 externally. Segmentation and QCA accuracies exceeded 0.92 in both cohorts. The complete system identifies stenotic lesions and their locations within 2 min. Clinical feedback indicated over 80% satisfaction. Our findings support the potential of this model to improve diagnostic accuracy and streamline clinical workflows in coronary angiography.

Authors

  • Kuo-Ting Tang
    Department of Cardiology, Chi Mei Medical Center, 901, Zhonghua Road, Yongkang District, Tainan, 710, Taiwan.
  • Jhih-Yuan Shih
    Department of Cardiology, Chi Mei Medical Center, 901, Zhonghua Road, Yongkang District, Tainan, 710, Taiwan.
  • Po-Chao Hsu
    Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital; ; Department of Internal Medicine, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
  • Chia-Te Liao
    Division of Cardiovascular Medicine, Chi Mei Medical Center, Tainan, Taiwan; Evidence-Based Medicine and Health Policy Center, Chi Mei Medical Center, Tainan, Taiwan; School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan. Electronic address: [email protected].
  • I-Min Chiu
    Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
  • Zhih-Cherng Chen
    Division of Cardiology, Department of Internal Medicine, Chi Mei Medical Center, No. 901, Zhonghua Rd., Yongkang Dist., Tainan City 710402, Taiwan.
  • Han-Lung Huang
    Department of Cardiology, Chi Mei Medical Center, 901, Zhonghua Road, Yongkang District, Tainan, 710, Taiwan.
  • Liang-Chuan Ou Yang
    Department of Information Technology, Chi Mei Medical Center, Tainan, Taiwan.
  • Chung Feng Liu
    Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan.
  • Jhi-Joung Wang
    Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan.
  • Chieh-Hung Chang
    Medical Product Division, Compal Electronics, Inc., Taipei, Taiwan.
  • Jen-Sheng Huang
    Medical Product Division, Compal Electronics, Inc., Taipei, Taiwan.
  • Yuan-Hsing Hsu
    Medical Product Division, Compal Electronics, Inc., Taipei, Taiwan.
  • Meng-Che Tsai
    Medical Product Division, Compal Electronics, Inc., Taipei, Taiwan.
  • Nien-Lun Chen
    Medical Product Division, Compal Electronics, Inc., Taipei, Taiwan.
  • Shih-Hsu Huang
    Medical Product Division, Compal Electronics, Inc., Taipei, Taiwan.
  • Kun-Sung Chen
    Medical Product Division, Compal Electronics, Inc., Taipei, Taiwan.
  • Wei-Ting Chang
    Department of Cardiology, Chi Mei Medical Center, 901, Zhonghua Road, Yongkang District, Tainan, 710, Taiwan. [email protected].

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