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

Authors :
Kim, Yoon Ho
Kim, Gwang Ha
Kim, Kwang Baek
Lee, Moon Won
Lee, Bong Eun
Baek, Dong Hoon
Kim, Do Hoon
Park, Jun Chul
Source :
Journal of Clinical Medicine; Oct2020, Vol. 9 Issue 10, p3162-3162, 1p
Publication Year :
2020

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 75.5%, 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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20770383
Volume :
9
Issue :
10
Database :
Complementary Index
Journal :
Journal of Clinical Medicine
Publication Type :
Academic Journal
Accession number :
146668731
Full Text :
https://doi.org/10.3390/jcm9103162