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Deep Learning Model for Coronary Angiography

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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.

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Data Availability

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher upon request.

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Funding

This work was supported by grants from the Department of Science and Technology of Jilin Province (No. 20200708112YY), the National Natural Science Foundation of China (No. 61972174 and 62172187).

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Authors and Affiliations

  1. Department of Cardiology, Second Hospital of Jilin University, Changchun, 130012, China

    Hao Ling, Yu Xiao, Hui Yan & Chunli Song

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Biqian Chen, Renchu Guan, Jingbo Chen, Xiaoyue Feng & Haoyu Pang

  3. Department of Cardiology, Sixth People’s Hospital, Shanghai Jiaotong University, Shanghai, 200233, China

    Qingyu Chen

  4. Department of Cardiology, the Eighth Affiliated Hospital of Sun Yat Sen University, Shenzhen, 518033, China

    Lianru Bi

Authors
  1. Hao Ling
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  2. Biqian Chen
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  3. Renchu Guan
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  4. Yu Xiao
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Correspondence to Chunli Song.

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Associate Editor Angela Taylor oversaw the review of this article

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Ling, H., Chen, B., Guan, R. et al. Deep Learning Model for Coronary Angiography. J. of Cardiovasc. Trans. Res. 16, 896–904 (2023). https://doi.org/10.1007/s12265-023-10368-8

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  • DOI: https://doi.org/10.1007/s12265-023-10368-8

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