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Development and validation of a convolutional neural network model for diagnosing Helicobacter pylori infections with endoscopic images: a multicenter study

Authors
 Ji Yeon Seo  ;  Hotak Hong  ;  Wi-Sun Ryu  ;  Dongmin Kim  ;  Jaeyoung Chun  ;  Min-Sun Kwak 
Citation
 GASTROINTESTINAL ENDOSCOPY, Vol.97(5) : 880-888.e2, 2023-05 
Journal Title
GASTROINTESTINAL ENDOSCOPY
ISSN
 0016-5107 
Issue Date
2023-05
MeSH
Endoscopy, Gastrointestinal / methods ; Helicobacter Infections* / diagnosis ; Helicobacter pylori* ; Humans ; Neural Networks, Computer
Abstract
Background and Aims: Insufficient validation limits the generalizability of deep learning in diagnosing Helicobacter pylori infection with endoscopic images. The aim of this study was to develop a deep learning model for the diagnosis of H pylori infection using endoscopic images and validate the model with internal and external datasets. Methods: A convolutional neural network (CNN) model was developed based on a training dataset comprising 13,403 endoscopic images from 952 patients who underwent endoscopy at Seoul National University Hospital Gangnam Center. Internal validation was performed using a separate dataset comprised of images of 411 individuals of Korean descent and 131 of non-Korean descent. External validation was performed with the images of 160 patients in Gangnam Severance Hospital. Gradient-weighted class activation mapping was performed to visually explain the model. Results: In predicting H pylori ever-infected status, the sensitivity, specificity, and accuracy of internal validation for people of Korean descent were .96 (95% confidence interval [CI], .93-.98), .90 (95% CI, .85-.95), and .94 (95% CI, .91-.96), respectively. In the internal validation for people of non-Korean descent, the sensitivity, specificity, and accuracy in predicting H pylori ever-infected status were .92 (95% CI, .86-.98), .79 (95% CI, .67-.91), and .88 (95% CI, .82-.93), respectively. In the external validation cohort, sensitivity, specificity, and accuracy were .86 (95% CI, .80-.93), .88 (95% CI, .79-.96), and .87 (95% CI, .82-.92), respectively, when performing 2-group categorization. Gradient-weighted class activation mapping showed that the CNN model captured the characteristic findings of each group. Conclusions: This CNN model for diagnosing H pylori infection showed good overall performance in internal and external validation datasets, particularly in categorizing patients into the never- versus ever-infected groups. © 2023 American Society for Gastrointestinal Endoscopy
Full Text
https://www.sciencedirect.com/science/article/pii/S001651072300010X
DOI
10.1016/j.gie.2023.01.007
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Chun, Jaeyoung(천재영) ORCID logo https://orcid.org/0000-0002-4212-0380
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/198020
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