51. High Accuracy of Convolutional Neural Network for Evaluation of Helicobacter pylori Infection Based on Endoscopic Images: Preliminary Experience
- Author
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Weiling Hu, Jianping Wang, Jingjing Li, Bin Ye, Guoliang Ye, John J. Kim, Jiquan Liu, Jianmin Si, Xinjian Zhu, Wenfang Zheng, Tao Yu, Songlin Luo, and Xu Zhang
- Subjects
Adult ,Male ,medicine.medical_specialty ,Helicobacter pylori infection ,Biopsy ,Pilot Projects ,Gastroenterology ,Endoscopy, Gastrointestinal ,Article ,Helicobacter Infections ,03 medical and health sciences ,0302 clinical medicine ,Deep Learning ,Internal medicine ,Gastroscopy ,medicine ,Image Processing, Computer-Assisted ,Humans ,Retrospective Studies ,Breath test ,Carbon Isotopes ,medicine.diagnostic_test ,biology ,Helicobacter pylori ,business.industry ,Area under the curve ,Retrospective cohort study ,Endoscopy ,Middle Aged ,biology.organism_classification ,Decision Support Systems, Clinical ,Confidence interval ,Breath Tests ,ROC Curve ,Gastric Mucosa ,030220 oncology & carcinogenesis ,030211 gastroenterology & hepatology ,Female ,business - Abstract
Objectives Application of artificial intelligence in gastrointestinal endoscopy is increasing. The aim of the study was to examine the accuracy of convolutional neural network (CNN) using endoscopic images for evaluating Helicobacter pylori (H. pylori) infection. Methods Patients who received upper endoscopy and gastric biopsies at Sir Run Run Shaw Hospital (January 2015-June 2015) were retrospectively searched. A novel Computer-Aided Decision Support System that incorporates CNN model (ResNet-50) based on endoscopic gastric images was developed to evaluate for H. pylori infection. Diagnostic accuracy was evaluated in an independent validation cohort. H. pylori infection was defined by the presence of H. pylori on immunohistochemistry testing on gastric biopsies and/or a positive 13C-urea breath test. Results Of 1,959 patients, 1,507 (77%) including 847 (56%) with H. pylori infection (11,729 gastric images) were assigned to the derivation cohort, and 452 (23%) including 310 (69%) with H. pylori infection (3,755 images) were assigned to the validation cohort. The area under the curve for a single gastric image was 0.93 (95% confidence interval [CI] 0.92-0.94) with sensitivity, specificity, and accuracy of 81.4% (95% CI 79.8%-82.9%), 90.1% (95% CI 88.4%-91.7%), and 84.5% (95% CI 83.3%-85.7%), respectively, using an optimal cutoff value of 0.3. Area under the curve for multiple gastric images (8.3 ± 3.3) per patient was 0.97 (95% CI 0.96-0.99) with sensitivity, specificity, and accuracy of 91.6% (95% CI 88.0%-94.4%), 98.6% (95% CI 95.0%-99.8%), and 93.8% (95% CI 91.2%-95.8%), respectively, using an optimal cutoff value of 0.4. Discussion In this pilot study, CNN using multiple archived gastric images achieved high diagnostic accuracy for the evaluation of H. pylori infection.
- Published
- 2019