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- 出生日期：1983-09-02
- 电子邮箱：zhaowang92@uestc.edu.cn
- 入职时间：2018-06-01
- 学历：博士研究生毕业
- 办公地点：清水河科研楼4号C区336房间
- 性别：男
- 学位：哲学博士学位
- 职称：教授
- 博士生导师
- 曾获荣誉：国家青年特聘专家，荣获四川省“天府峨眉计划”、成都市“四派人才”、“蓉漂计划”称号
- 学科：电子科学与技术
物理电子学
Coronary artery calcification and cardiovascular outcome as assessed by intravascular OCT and artificial intelligence
- 点击次数：
- 所属单位：[1]Harbin Med Univ, Affiliated Hosp 2, Dept Cardiol, Harbin, Peoples R China;[2]Univ Elect Sci & Technol China, Sch Elect Sci & Engn, Chengdu, Peoples R China;[3]Dalian Med Univ, Affiliated Hosp 1, Dept Cardiol, Dalian, Peoples R China;[4]Tokyo Med & Dent Univ, Dept Cardiovasc Med, Tokyo, Japan;[5]Univ Cattolica Sacro Cuore, Dept Cardiovasc Med, Rome, Italy
- 发表刊物：BIOMEDICAL OPTICS EXPRESS
- 关键字：Biomineralization - Bone - Calcification (biochemistry) - Image segmentation - Learning systems
- 摘要：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.
- 文献类型：Article
- 卷号：15
- 期号：8
- 页面范围：4438-4452
- ISSN号：2156-7085
- 是否译文：否