A deep learning algorithm for detecting acute myocardial infarction

医学 心肌梗塞 接收机工作特性 急诊科 内科学 诊断准确性 心脏病学 心电图 肌钙蛋白 回顾性队列研究 机器学习 曲线下面积 急诊分诊台 队列 曲线下面积 急诊医学 算法 精神科 计算机科学 药代动力学
作者
Wencheng Liu,Chin‐Sheng Lin,Chien‐Sung Tsai,Tien‐Ping Tsao,Cheng-Chung Cheng,Jun‐Ting Liou,Wei‐Shiang Lin,Shu‐Meng Cheng,Yu-Sheng Lou,Chia-Cheng Lee,Chin Lin
出处
期刊:Eurointervention [European Association of Percutaneous Cardiovascular Interventions]
卷期号:17 (9): 765-773 被引量:45
标识
DOI:10.4244/eij-d-20-01155
摘要

Delayed diagnosis or misdiagnosis of acute myocardial infarction (AMI) is not unusual in daily practice. Since a 12-lead electrocardiogram (ECG) is crucial for the detection of AMI, a systematic algorithm to strengthen ECG interpretation may have important implications for improving diagnosis.We aimed to develop a deep learning model (DLM) as a diagnostic support tool based on a 12-lead electrocardiogram.This retrospective cohort study included 1,051/697 ECGs from 737/287 coronary angiogram (CAG)-validated STEMI/NSTEMI patients and 140,336 ECGs from 76,775 non-AMI patients at the emergency department. The DLM was trained and validated in 80% and 20% of these ECGs. A human-machine competition was conducted. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the performance of the DLM.The AUC of the DLM for STEMI detection was 0.976 in the human-machine competition, which was significantly better than that of the best physicians. Furthermore, the DLM independently demonstrated sufficient diagnostic capacity for STEMI detection (AUC=0.997; sensitivity, 98.4%; specificity, 96.9%). Regarding NSTEMI detection, the AUC of the combined DLM and conventional cardiac troponin I (cTnI) increased to 0.978, which was better than that of either the DLM (0.877) or cTnI (0.950).The DLM may serve as a timely, objective and precise diagnostic decision support tool to assist emergency medical system-based networks and frontline physicians in detecting AMI and subsequently initiating reperfusion therapy.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
邢文瑞发布了新的文献求助10
刚刚
小鱼儿完成签到,获得积分10
1秒前
1秒前
上官若男应助lh采纳,获得30
2秒前
柯一一应助千灯采纳,获得10
2秒前
乐乐应助young采纳,获得10
2秒前
3秒前
6秒前
小李博士发布了新的文献求助10
7秒前
感动鞋垫发布了新的文献求助10
8秒前
菲菲发布了新的文献求助10
8秒前
paul发布了新的文献求助10
10秒前
10秒前
10秒前
任性的数据线完成签到,获得积分10
12秒前
JamesPei应助小李博士采纳,获得10
13秒前
完美世界应助超人也读博采纳,获得10
14秒前
14秒前
费笑柳发布了新的文献求助10
14秒前
15秒前
15秒前
paul完成签到,获得积分10
16秒前
17秒前
ding应助奔波儿灞采纳,获得10
18秒前
pangao完成签到,获得积分10
18秒前
18秒前
Prime完成签到,获得积分10
18秒前
wanci应助科研通管家采纳,获得10
20秒前
Lucas应助科研通管家采纳,获得10
20秒前
FashionBoy应助科研通管家采纳,获得30
20秒前
乐观小之应助科研通管家采纳,获得10
20秒前
20秒前
英俊的铭应助科研通管家采纳,获得10
20秒前
我是老大应助科研通管家采纳,获得10
20秒前
斯文败类应助紫心采纳,获得10
20秒前
彭于晏应助科研通管家采纳,获得10
20秒前
我是老大应助科研通管家采纳,获得10
20秒前
科目三应助科研通管家采纳,获得10
21秒前
Prime发布了新的文献求助10
21秒前
思源应助科研通管家采纳,获得10
21秒前
高分求助中
Ophthalmic Equipment Market by Devices(surgical: vitreorentinal,IOLs,OVDs,contact lens,RGP lens,backflush,diagnostic&monitoring:OCT,actorefractor,keratometer,tonometer,ophthalmoscpe,OVD), End User,Buying Criteria-Global Forecast to2029 2000
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3962851
求助须知:如何正确求助?哪些是违规求助? 3508777
关于积分的说明 11143063
捐赠科研通 3241643
什么是DOI,文献DOI怎么找? 1791638
邀请新用户注册赠送积分活动 873002
科研通“疑难数据库(出版商)”最低求助积分说明 803577