Artificial intelligence diagnosis of patent foramen ovale in contrast transthoracic echocardiography

Artificial intelligence (AI) is rarely directly used in patent foramen ovale (PFO) diagnosis. In this study, an AI model was developed to detect the presence of PFO automatically in both contrast transthoracic echocardiography (cTTE) images and videos. The whole intelligent diagnosis neural network framework consists of two functional modules of image segmentation (Unet, <i>n</i> = 1866) and image classification (ResNet 101, <i>n</i> = 9152). Finally, another test databases, including 20 cTTE videos (4609 cTTE images), was used to compare the RLS classification model accuracy between AI model and different levels of physicians. The Dice similarity coefficient of left chamber segmentation model of cTTE images was 91.41%, the accuracy of PFO-RLS classification model of cTTE images was 83.55%, the accuracy of PFO-RLS classification model of cTTE videos was 90%. Besides, the AI diagnosis time was significantly shorter than doctors (at only 1.3 s).

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Publication Details

Journal
iScience
Published
2024-09-21
DOI
https://doi.org/10.1016/j.isci.2024.111012
Citations
1
Primary Topic
Cardiovascular and Diving-Related Complications
Type
article
Field-Weighted Citation Impact
0.42

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article

Artificial intelligence diagnosis of patent foramen ovale in contrast transthoracic echocardiography

Xiaofang Zhong, Jinfeng Xu, Yuxiang Huang, Yuanyuan Sheng et al.
1 citations
iScience
Cardiovascular and Diving-Related Complications
0.42
article

Artificial intelligence diagnosis of patent foramen ovale in contrast transthoracic echocardiography

Xiaofang Zhong, Jinfeng Xu, Yuxiang Huang, Yuanyuan Sheng, Guijuan Peng, Xiaohua Liu, Jian Li, Xiaoxuan Lin, Lixin Chen, Mengjie Gu, Shuyu Luo, Zhaohui Ning, Bobo Shi, Qian Liu, Lin Wang, Yingying Liu
article en
1 citations

Abstract

Artificial intelligence (AI) is rarely directly used in patent foramen ovale (PFO) diagnosis. In this study, an AI model was developed to detect the presence of PFO automatically in both contrast transthoracic echocardiography (cTTE) images and videos. The whole intelligent diagnosis neural network framework consists of two functional modules of image segmentation (Unet, <i>n</i> = 1866) and image classification (ResNet 101, <i>n</i> = 9152). Finally, another test databases, including 20 cTTE videos (4609 cTTE images), was used to compare the RLS classification model accuracy between AI model and different levels of physicians. The Dice similarity coefficient of left chamber segmentation model of cTTE images was 91.41%, the accuracy of PFO-RLS classification model of cTTE images was 83.55%, the accuracy of PFO-RLS classification model of cTTE videos was 90%. Besides, the AI diagnosis time was significantly shorter than doctors (at only 1.3 s).

iScienceVol. 27(11)
Jinan University (CN), Henan University of Science and Technology (CN), Southern University of Science and Technology (CN), First Affiliated Hospital of Henan University of Science and Technology (CN)
National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province
Openalex Percentile: Top 33%
Cardiovascular and Diving-Related Complications
0.42
DOIOpen Access PDFOpenAlex
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