CC BY-NC-ND 4.0 · Endosc Int Open 2018; 06(02): E139-E144
DOI: 10.1055/s-0043-120830
Original article
Eigentümer und Copyright ©Georg Thieme Verlag KG 2018

Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal endoscopy images

Takumi Itoh
1  Department of Medical System Engineering, Graduate School of Engineering, Chiba University
,
Hiroshi Kawahira
2  Center for Frontier Medical Engineering, Chiba University, Chiba, Japan
3  Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
,
Hirotaka Nakashima
4  Department of Gastroenterology, Foundation for Detection of Early Gastric Carcinoma, Tokyo, Japan
,
Noriko Yata
5  Department of Information Processing and Computer Science, Graduate School of Advanced Integration Science, Chiba University, Chiba, Japan
› Author Affiliations
Further Information

Publication History

submitted 12 September 2017

accepted after revision 22 September 2017

Publication Date:
01 February 2018 (online)

Abstract

Background and study aims Helicobacter pylori (HP)-associated chronic gastritis can cause mucosal atrophy and intestinal metaplasia, both of which increase the risk of gastric cancer. The accurate diagnosis of HP infection during routine medical checks is important. We aimed to develop a convolutional neural network (CNN), which is a machine-learning algorithm similar to deep learning, capable of recognizing specific features of gastric endoscopy images. The goal behind developing such a system was to detect HP infection early, thus preventing gastric cancer.

Patients and methods For the development of the CNN, we used 179 upper gastrointestinal endoscopy images obtained from 139 patients (65 were HP-positive: ≥ 10 U/mL and 74 were HP-negative: < 3 U/mL on HP IgG antibody assessment). Of the 179 images, 149 were used as training images, and the remaining 30 (15 from HP-negative patients and 15 from HP-positive patients) were set aside to be used as test images. The 149 training images were subjected to data augmentation, which yielded 596 images. We used the CNN to create a learning tool that would recognize HP infection and assessed the decision accuracy of the CNN with the 30 test images by calculating the sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC).

Results The sensitivity and specificity of the CNN for the detection of HP infection were 86.7 % and 86.7 %, respectively, and the AUC was 0.956.

Conclusions CNN-aided diagnosis of HP infection seems feasible and is expected to facilitate and improve diagnosis during health check-ups.