Integrated deep learning model for automatic detection and classification of stenosis in coronary angiography

IF 2.6 4区 生物学 Q2 BIOLOGY Computational Biology and Chemistry Pub Date : 2024-08-24 DOI:10.1016/j.compbiolchem.2024.108184
Tao Wang , Xiaojun Su , Yuchao Liang , Xu Luo , Xiao Hu , Ting Xia , Xuebin Ma , Yongchun Zuo , Huilin Xia , Lei Yang
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Abstract

Coronary artery disease poses a significant threat to human health. In clinical settings, coronary angiography remains the gold standard for diagnosing coronary heart disease. A crucial aspect of this diagnosis involves detecting arterial narrowings. Categorizing these narrowings can provide insight into whether patients should receive vascular revascularization treatment. The majority of current deep learning methods for analyzing coronary angiography are mostly confined to the theoretical research domain, with limited studies offering direct practical support to clinical practitioners. This paper proposes an integrated deep-learning model for the localization and classification of narrowings in coronary angiography images. The experimentation employed 1606 coronary angiography images obtained from 132 patients, resulting in an accuracy of 88.9 %, a recall rate of 85.4 %, an F1 score of 0.871, and a MAP value of 0.875 for vascular stenosis detection. Furthermore, we developed the "Hemadostenosis" web platform (http://bioinfor.imu.edu.cn/hemadostenosis) using Django, a highly mature HTTP framework. Users are able to submit coronary angiography image data for assessment via a visual interface. Subsequently, the system sends the images to a trained convolutional neural network model to localize and categorize the narrowings. Finally, the visualized outcomes are displayed to users and are downloadable. Our proposed approach pioneers the recognition and categorization of arterial narrowings in vascular angiography, offering practical support to clinical practitioners in their learning and diagnostic processes.

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用于自动检测和分类冠状动脉造影狭窄的集成深度学习模型
冠状动脉疾病对人类健康构成重大威胁。在临床上,冠状动脉造影术仍然是诊断冠心病的黄金标准。诊断的一个重要方面是检测动脉狭窄。对这些狭窄进行分类,可以帮助我们了解患者是否应该接受血管重建治疗。目前用于分析冠状动脉造影的深度学习方法大多局限于理论研究领域,为临床从业人员提供直接实际支持的研究十分有限。本文提出了一种用于冠状动脉造影图像狭窄定位和分类的集成深度学习模型。实验采用了来自 132 名患者的 1606 张冠状动脉造影图像,结果显示血管狭窄检测的准确率为 88.9%,召回率为 85.4%,F1 得分为 0.871,MAP 值为 0.875。此外,我们还利用高度成熟的 HTTP 框架 Django 开发了 "血管狭窄 "网络平台 (http://bioinfor.imu.edu.cn/hemadostenosis)。用户可以通过可视化界面提交冠状动脉造影图像数据进行评估。随后,系统将图像发送到训练有素的卷积神经网络模型,对狭窄进行定位和分类。最后,可视化结果显示给用户并可下载。我们提出的方法开创了血管造影术中动脉狭窄识别和分类的先河,为临床从业人员的学习和诊断过程提供了实用支持。
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来源期刊
Computational Biology and Chemistry
Computational Biology and Chemistry 生物-计算机:跨学科应用
CiteScore
6.10
自引率
3.20%
发文量
142
审稿时长
24 days
期刊介绍: Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.
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