The efficiency of a Machine learning approach based on Spatio-Temporal information in the detection of patent foramen ovale from contrast transthoracic echocardiography Images: A primary study

作者全名："Yang, Jing; Zhang, Shiquan; Zhou, Yixi; Yu, Hangyuan; Zhang, Huiqin; Lan, Tingyu; Zhang, Meng; Huang, Wenyan; Zhang, Wei; Cheng, Linggang; Li, Yongjia; Tian, Jiawei; Yuan, Jianjun; Ran, Haitao; Guo, Yanli; Zhang, Ruifang; Zhang, Hongxia; Wang, Anxin; Du, Lijuan; He, Wen"

作者地址："[Yang, Jing; Zhou, Yixi; Yu, Hangyuan; Zhang, Huiqin; Lan, Tingyu; Zhang, Meng; Huang, Wenyan; Zhang, Wei; Cheng, Linggang; Li, Yongjia; Zhang, Hongxia; Du, Lijuan; He, Wen] Capital Med Univ, Beijing Tiantan Hosp, Dept Ultrasound, 119 West Rd South 4th Ring Rd, Beijing 100160, Peoples R China; [Yang, Jing] Sichuan Univ, West China Hosp 2, Dept Ultrasound, 1416, Sect 1,Chenglong Ave, Chengdu 610041, Peoples R China; [Zhang, Shiquan] Chinese Acad Sci, Shenzhen Inst Adv Technol, 1068 Xueyuan Ave, Shenzhen 518055, Peoples R China; [Zhang, Shiquan] Univ Chinese Acad Sci, Beijing 100864, Peoples R China; [Wang, Anxin] Capital Med Univ, Beijing Tiantan Hosp, China Natl Clin Res Ctr Neurol Dis, 119 West Rd South 4th Ring Rd, Beijing 100160, Peoples R China; [Tian, Jiawei] Harbin Med Univ, Affiliated Hosp 2, Dept Ultrasound, 246 Xuefu Rd, Harbin 100160, Peoples R China; [Yuan, Jianjun] Henan Peoples Hosp, Dept Ultrasound, 7 Weiwu Rd, Zhengzhou 450003, Peoples R China; [Ran, Haitao] Chongqing Med Univ, Affiliated Hosp 2, Dept Ultrasound, 76 Linjiang Rd, Chongqing 400010, Peoples R China; [Guo, Yanli] Third Mil Med Univ, Southwest Hosp, Dept Ultrasound, 30 Gaotanyan Zhengjie, Chongqing 400038, Peoples R China; [Zhang, Ruifang] Zhengzhou Univ, Affiliated Hosp 1, Dept Ultrasound, 1 East Jianshe Rd, Zhengzhou 450052, Peoples R China"

通信作者："Du, LJ; He, W (通讯作者)，Capital Med Univ, Beijing Tiantan Hosp, Dept Ultrasound, 119 West Rd South 4th Ring Rd, Beijing 100160, Peoples R China."

来源：BIOMEDICAL SIGNAL PROCESSING AND CONTROL

ESI学科分类：ENGINEERING

WOS号：WOS:000951893300001

JCR分区：Q1

影响因子：4.9

年份：2023

卷号：84

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：Artificial intelligence (AI); Contrast transthoracic echocardiography; Patent foramen ovale; Diagnosis

摘要："Objectives: As a noninvasive method, diagnostic rate of PFO by CTTE is controversial because of its subjectivity and the examiner's dependence about the bubbles identification. In order to improve the diagnostic accuracy, a machine learning algorithm diagnostic method for PFO based on CTTE images (AI-CTTE) was established in our primary study. This study aimed to investigate the efficiency of the AI method in PFO detect,and to clarify the clinical value of AI in identifying PFO.Methods: From August 2019 to June 2021, 200 patients with suspected PFO in six hospitals were eventually enrolled, and all the patients underwent CTEE and CTTE examinations respectively. Each case contained ul-trasound videos of 5 cardiac cycles before and after injecting agitated saline with Valsalva maneuver from apical four-chamber (A4C) view. The patients with or without PFO were identified by the machine learning algorithm and other non-operating physicians (including experts and junior physicians). Results: A total of 144 out of 200 (72%) patients were confirmed PFO by CTEE. CTEE was used as a gold standard to assess the model's diagnostic valve.The sensitivity and specificity of the AI-CTTE for PFO detection were 78% and 75% respectively. In comparison, the sensitivity and specificity of the ultrasonic experts group were 86% and 89%, while those of the ultrasound residents group were 62% and 82%. The diagnostic efficiency of the AI-CTTE was similar with the ultrasonic experts group, but significantly higher than the ultrasound residents group.Conclusions: As a non-invasive method, AI can provide diagnostic help for junior physicians, it creates a new way for PFO recognition."

基金机构：Beijing Hospital Authority Research and Development Program [PX2018020]; National Natural Science Fundation of China [81730050]

基金资助正文："This work was funded by two projects, the Beijing Hospital Authority Research and Development Program (PX2018020) and the National Natural Science Fundation of China (81730050) . We would like to ex- press our gratitude to all the authors who made this special issue possible."