Deep Learning Model for Coronary Angiography.

Journal: Journal of cardiovascular translational research
PMID: 36928587

Abstract

The visual inspection of coronary artery stenosis is known to be significantly affected by variation, due to the presence of other tissues, camera movements, and uneven illumination. More accurate and intelligent coronary angiography diagnostic models are necessary for improving the above problems. In this study, 2980 medical images from 949 patients are collected and a novel deep learning-based coronary angiography (DLCAG) diagnose system is proposed. Firstly, we design a module of coronary classification. Then, we introduce RetinaNet to balance positive and negative samples and improve the recognition accuracy. Additionally, DLCAG adopts instance segmentation to segment the stenosis of vessels and depict the degree of the stenosis vessels. Our DLCAG is available at http://101.132.120.184:8077/ . When doctors use our system, all they need to do is login to the system, upload the coronary angiography videos. Then, a diagnose report is automatically generated.

Authors

  • Hao Ling
    Department of General Surgery, Third Xiangya Hospital, Central South University, 138 Tongzipo Street, Changsha, 410013, Hunan, China.
  • Biqian Chen
    Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
  • Renchu Guan
    Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
  • Yu Xiao
    Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, the Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
  • Hui Yan
    School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, China. Electronic address: yanhui@mail.njust.edu.cn.
  • Qingyu Chen
    Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA.
  • Lianru Bi
    Department of Cardiology, the Eighth Affiliated Hospital of Sun Yat Sen University, Shenzhen, 518033, China.
  • Jingbo Chen
    School of Materials Science and Engineering, Henan Key Laboratory of Advanced Nylon Materials and Application, Henan Innovation Center for Functional Polymer Membrane Materials, Zhengzhou University, Zhengzhou, 450001, China. lsrzzdx@zzu.edu.cn.
  • Xiaoyue Feng
    Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, 130012, Changchun, China.
  • Haoyu Pang
    Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
  • Chunli Song
    Department of Nutrition and Food Hygiene, School of Public Health, Medical College of Soochow University, Suzhou 215123, China.

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