Hiroshi Kawahira

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.

Hedgehog signaling is known to regulate tissue morphogenesis and cell differentiation in a dose-dependent manner. Loss of Indian hedgehog (Ihh) results in reduction in pancreas size, indicating a requirement for hedgehog signaling during pancreas development. By contrast, ectopic expression of sonic hedgehog (Shh) inhibits pancreatic marker expression and results in transformation of pancreatic mesenchyme into duodenal mesoderm. These observations suggest that hedgehog signaling activity has to be regulated tightly to ensure proper pancreas development. We have analyzed the function of two hedgehog inhibitors, Hhip and patched 1 (Ptch), during pancreas formation. Our results indicated that loss of Hhip results in increased hedgehog signaling within the pancreas anlage. Pancreas morphogenesis, islet formation and endocrine cell proliferation is impaired in Hhip mutant embryos. Additional loss of one Ptch allele in Hhip-/-Ptch+/- embryos further impairs pancreatic growth and endodermal cell differentiation. These results demonstrate combined requirements for Hhip and Ptch during pancreas development and point to a dose-dependent response to hedgehog signaling within pancreatic tissue. Reduction of Fgf10 expression in Hhip homozygous mutants suggests that at least some of the observed phenotypes result from hedgehog-mediated inhibition of Fgf signaling at early stages.

OBJECTIVE: This study aimed to use convolutional neural network (CNN), a deep learning software, to assist in cT1b diagnosis. METHODS: This retrospective study used 190 colon lesion images from 41 cases of colon endoscopies performed between February 2015 and October 2016. Unenhanced colon endoscopy images (520 × 520 pixels) with white light were used. Images included 14 cTis cases with endoscopic resection and 14 cT1a and 13 cT1b cases with surgical resection. Protruding, flat, and recessed lesions were analyzed. AlexNet and Caffe were used for machine learning. Fine tuning of data to increase image numbers was performed. Oversampling for the training images was conducted to avoid impartiality in image numbers, and learning was carried out. The 3-fold cross-validation method was used. Sensitivity, specificity, accuracy, and area under the curve (AUC) values in the receiver operating characteristic curve were calculated for each group. RESULTS: The results were the average of obtained values. With CNN learning, cT1b sensitivity, specificity, and accuracy were 67.5, 89.0, and 81.2%, respectively, and AUC was 0.871. CONCLUSION: Quantitative diagnosis is possible using an endoscopic diagnostic support system with machine learning, without relying on the skill and experience of endoscopists. Moreover, this system could be used to objectively evaluate endoscopic diagnoses.