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.
