Coronary artery calcification and cardiovascular outcome as assessed by intravascular OCT and artificial intelligence

IF 2.9 2区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Biomedical optics express Pub Date : 2024-06-17 DOI:10.1364/boe.524946

Jinwei Tian, Chao Li, Zhifeng Qin, Yanwen Zhang, Qinglu Xu, Yuqi Zheng, Xiangyu Meng, Peng Zhao, Kaiwen Li, Suhong Zhao, Shan Zhong, Xinyu Hou, Xiang Peng, Yuxin Yang, Yu Liu, Songzhi Wu, Yidan Wang, Xiangwen Xi, Yanan Tian, Wenbo Qu, Na Sun, Fan Wang, Yan Wang, Jie Xiong, Xiaofang Ban, Taishi Yonetsu, Rocco Vergallo, Bo Zhang, Bo Yu, Zhao Wang

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引用次数: 0

Abstract

Coronary artery calcification (CAC) is a marker of atherosclerosis and is thought to be associated with worse clinical outcomes. However, evidence from large-scale high-resolution imaging data is lacking. We proposed a novel deep learning method that can automatically identify and quantify CAC in massive intravascular OCT data trained using efficiently generated sparse labels. 1,106,291 OCT images from 1,048 patients were collected and utilized to train and evaluate the method. The Dice similarity coefficient for CAC segmentation and the accuracy for CAC classification are 0.693 and 0.932, respectively, close to human-level performance. Applying the method to 1259 ST-segment elevated myocardial infarction patients imaged with OCT, we found that patients with a greater extent and more severe calcification in the culprit vessels were significantly more likely to have major adverse cardiovascular and cerebrovascular events (MACCE) (p < 0.05), while the CAC in non-culprit vessels did not differ significantly between MACCE and non-MACCE groups.

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通过血管内 OCT 和人工智能评估冠状动脉钙化和心血管预后

冠状动脉钙化（CAC）是动脉粥样硬化的标志物，被认为与较差的临床预后有关。然而，目前还缺乏来自大规模高分辨率成像数据的证据。我们提出了一种新颖的深度学习方法，它能在使用高效生成的稀疏标签训练的海量血管内 OCT 数据中自动识别和量化 CAC。我们收集了来自 1,048 名患者的 1,106,291 张 OCT 图像，并利用这些图像对该方法进行了训练和评估。CAC 分割的 Dice 相似系数和 CAC 分类的准确率分别为 0.693 和 0.932，接近人类水平。将该方法应用于 1259 例 ST 段抬高的心肌梗死患者的 OCT 图像，我们发现，罪魁祸首血管钙化范围更大、更严重的患者发生重大不良心脑血管事件（MACCE）的可能性明显更高（p <0.05），而非罪魁祸首血管的 CAC 在 MACCE 组和非 MACCE 组之间没有显著差异。

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来源期刊

Biomedical optics express BIOCHEMICAL RESEARCH METHODS-OPTICS

CiteScore

6.80

自引率

11.80%

发文量

633

审稿时长

1 months

期刊介绍： The journal''s scope encompasses fundamental research, technology development, biomedical studies and clinical applications. BOEx focuses on the leading edge topics in the field, including: Tissue optics and spectroscopy Novel microscopies Optical coherence tomography Diffuse and fluorescence tomography Photoacoustic and multimodal imaging Molecular imaging and therapies Nanophotonic biosensing Optical biophysics/photobiology Microfluidic optical devices Vision research.