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Application of A Convolutional Neural Network in The Diagnosis of Gastric Mesenchymal Tumors on Endoscopic Ultrasonography Images

Authors
 Yoon Ho Kim  ;  Gwang Ha Kim  ;  Kwang Baek Kim  ;  Moon Won Lee  ;  Bong Eun Lee  ;  Dong Hoon Baek  ;  Do Hoon Kim  ;  Jun Chul Park 
Citation
 JOURNAL OF CLINICAL MEDICINE, Vol.9(10) : 3162, 2020-10 
Journal Title
JOURNAL OF CLINICAL MEDICINE
Issue Date
2020-10
Keywords
artificial intelligence ; endoscopic ultrasonography ; gastrointestinal stromal tumor ; mesenchymal tumor ; stomach
Abstract
Background and aims: Endoscopic ultrasonography (EUS) is a useful diagnostic modality for evaluating gastric mesenchymal tumors; however, differentiating gastrointestinal stromal tumors (GISTs) from benign mesenchymal tumors such as leiomyomas and schwannomas remains challenging. For this reason, we developed a convolutional neural network computer-aided diagnosis (CNN-CAD) system that can analyze gastric mesenchymal tumors on EUS images.

Methods: A total of 905 EUS images of gastric mesenchymal tumors (pathologically confirmed GIST, leiomyoma, and schwannoma) were used as a training dataset. Validation was performed using 212 EUS images of gastric mesenchymal tumors. This test dataset was interpreted by three experienced and three junior endoscopists.

Results: The sensitivity, specificity, and accuracy of the CNN-CAD system for differentiating GISTs from non-GIST tumors were 83.0%, 75.5%, and 79.2%, respectively. Its diagnostic specificity and accuracy were significantly higher than those of two experienced and one junior endoscopists. In the further sequential analysis to differentiate leiomyoma from schwannoma in non-GIST tumors, the final diagnostic accuracy of the CNN-CAD system was 72.5%, which was significantly higher than that of two experienced and one junior endoscopists.

Conclusions: Our CNN-CAD system showed high accuracy in diagnosing gastric mesenchymal tumors on EUS images. It may complement the current clinical practices in the EUS diagnosis of gastric mesenchymal tumors.
Files in This Item:
T202007243.pdf Download
DOI
10.3390/jcm9103162
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Park, Jun Chul(박준철) ORCID logo https://orcid.org/0000-0001-8018-0010
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/183927
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