Ling Hao, Chen Biqian, Guan Renchu, Xiao Yu, Yan Hui, Chen Qingyu, Bi Lianru, Chen Jingbo, Feng Xiaoyue, Pang Haoyu, Song Chunli
Department of Cardiology, Second Hospital of Jilin University, Changchun, 130012, China.
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
J Cardiovasc Transl Res. 2023 Aug;16(4):896-904. doi: 10.1007/s12265-023-10368-8. Epub 2023 Mar 16.
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.
由于存在其他组织、摄像头移动以及光照不均等因素，冠状动脉狭窄的视觉检查已知会受到显著影响。为改善上述问题，需要更准确、智能的冠状动脉造影诊断模型。在本研究中，收集了来自949名患者的2980张医学图像，并提出了一种基于深度学习的新型冠状动脉造影（DLCAG）诊断系统。首先，我们设计了一个冠状动脉分类模块。然后，引入RetinaNet来平衡正负样本并提高识别准确率。此外，DLCAG采用实例分割来分割血管狭窄部位并描绘狭窄血管的程度。我们的DLCAG可在http://101.132.120.184:8077/获取。医生使用我们的系统时，他们只需登录系统，上传冠状动脉造影视频。然后，会自动生成一份诊断报告。
