Automatic echocardiographic evaluation of the probability of pulmonary hypertension using machine learning

Author:

Liao Zuwei12,Liu Kaikai3,Ding Shangwei4,Zhao Qinhua5,Jiang Yong67,Wang Lan5,Huang Taoran12,Yang LiFang2,Luo Dongling2,Zhang Erlei3,Zhang Yu3,Zhang Caojin28ORCID,Xu Xiaowei28,Fei Hongwen128ORCID

Affiliation:

1. Shantou University Medical College Shantou Guangdong China

2. Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou Guangdong China

3. School of Information Engineering Northwest A&F University Yangling Shanxi China

4. Department of Ultrasound The First Affiliated Hospital of Guangzhou Medical University Guangzhou Guangdong China

5. Department of Pulmonary Circulation Shanghai Pulmonary Hospital, Tongji University School of Medicine Shanghai China

6. State Key Laboratory of Cardiovascular Disease, Department of Echocardiography National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China

7. Department of Echocardiography Fuwai Hospital Chinese Academy of Medical Sciences Shenzhen China

8. Guangdong Provincial Key Laboratory of South China Structural Heart Disease Guangzhou Guangdong China

Abstract

AbstractEchocardiography, a simple and noninvasive tool, is the first choice for screening pulmonary hypertension (PH). However, accurate assessment of PH, incorporating both the pulmonary artery pressures and additional signs for PH remained unsatisfied. Thus, this study aimed to develop a machine learning (ML) model that can automatically evaluate the probability of PH. This cohort included data from 346 (275 for training set and internal validation set and 71 for external validation set) patients with suspected PH patients and receiving right heart catheterization. Echocardiographic images on parasternal short axis‐papillary muscle level (PSAX‐PML) view from all patients were collected, labeled, and preprocessed. Local features from each image were extracted and subsequently integrated to build a ML model. By adjusting the parameters of the model, the model with the best prediction effect is finally constructed. We used receiver‐operating characteristic analysis to evaluate model performance and compared the ML model with the traditional methods. The accuracy of the ML model for diagnosis of PH was significantly higher than the traditional method (0.945 vs. 0.892, p = 0.027 [area under the curve [AUC]]). Similar findings were observed in subgroup analysis and validated in the external validation set (AUC = 0.950 [95% CI: 0.897−1.000]). In summary, ML methods could automatically extract features from traditional PSAX‐PML view and automatically assess the probability of PH, which were found to outperform traditional echocardiographic assessments.

Publisher

Wiley

Subject

Pulmonary and Respiratory Medicine

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3