Application of Convolutional Neural Networks in the Diagnosis of Helicobacter pylori Infection Based on Endoscopic Images

Shichijo, Satoki; Nomura, Shuhei; Aoyama, Kazuharu; Nishikawa, Yoshitaka; Miura, Motoi; Shinagawa, Takahiro; Takiyama, Hirotoshi; Tanimoto, Tetsuya; Ishihara, Soichiro; Matsuo, Keigo; Tada, Takeshi

doi:10.1016/j.ebiom.2017.10.014

Cited by 246 publications

(203 citation statements)

References 24 publications

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“…There have been reports on machine learning and image diagnostic support using deep learning methods as one type of CNN [17,[31][32][33]. In our study, while the accuracy rate of diagnosis was high, cT1b sensitivity was high and specificity was low.…”

Section: Discussionmentioning

confidence: 54%

Endoscopic Diagnostic Support System for cT1b Colorectal Cancer Using Deep Learning

Ito

Kawahira

Nakashima³

et al. 2018

Oncology

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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.

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Section: Discussionmentioning

confidence: 54%

Endoscopic Diagnostic Support System for cT1b Colorectal Cancer Using Deep Learning

Ito

Kawahira

Nakashima³

et al. 2018

Oncology

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“…In recent years, AI using deep learning (especially for convolutional neural networks; CNN) has been applied to gastroenterology . However, CNN have a disadvantage in that it is difficult to investigate the rationale for a diagnosis (e.g.…”

Section: Introductionmentioning

confidence: 99%

Potential of automatic diagnosis system with linked color imaging for diagnosis of Helicobacter pylori infection

Yasuda

Hiroyasu

Hiwa

et al. 2019

Digestive Endoscopy

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Background and Aim It is necessary to establish universal methods for endoscopic diagnosis of Helicobacter pylori (HP) infection, such as computer‐aided diagnosis. In the present study, we propose a multistage diagnosis algorithm for HP infection. Methods The aims of this study are to: (i) to construct an interpretable automatic diagnostic system using a support vector machine for HP infection; and (ii) to compare the diagnosis capability of our artificial intelligence (AI) system with that of endoscopists. Presence of an HP infection determined through linked color imaging (LCI) was learned through machine learning. Trained classifiers automatically diagnosed HP‐positive and ‐negative patients examined using LCI. We retrospectively analyzed the new images from 105 consecutive patients; 42 were HP positive, 46 were post‐eradication, and 17 were uninfected. Five endoscopic images per case taken from different areas were read into the AI system, and used in the HP diagnosis. Results Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the diagnosis of HP infection using the AI system were 87.6%, 90.4%, 85.7%, 80.9%, and 93.1%, respectively. Accuracy of the AI system was higher than that of an inexperienced doctor, but there was no significant difference between the diagnosis of experienced physicians and the AI system. Conclusions The AI system can diagnose an HP infection with significant accuracy. There remains room for improvement, particularly for the diagnosis of post‐eradication patients. By learning more images and considering a diagnosis algorithm for post‐eradication patients, our new AI system will provide diagnostic support, particularly to inexperienced physicians.

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“…In 2017, Shichijo et al . developed a 22‐layer deep CNN to predict HP infection during ongoing gastroscopy.…”

Section: Stomachmentioning

confidence: 99%

Artificial intelligence and upper gastrointestinal endoscopy: Current status and future perspective

Mori

Kudo

Mohmed

et al. 2019

Digestive Endoscopy

112

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With recent breakthroughs in artificial intelligence, computer‐aided diagnosis (CAD) for upper gastrointestinal endoscopy is gaining increasing attention. Main research focuses in this field include automated identification of dysplasia in Barrett's esophagus and detection of early gastric cancers. By helping endoscopists avoid missing and mischaracterizing neoplastic change in both the esophagus and the stomach, these technologies potentially contribute to solving current limitations of gastroscopy. Currently, optical diagnosis of early‐stage dysplasia related to Barrett's esophagus can be precisely achieved only by endoscopists proficient in advanced endoscopic imaging, and the false‐negative rate for detecting gastric cancer is approximately 10%. Ideally, these novel technologies should work during real‐time gastroscopy to provide on‐site decision support for endoscopists regardless of their skill; however, previous studies of these topics remain ex vivo and experimental in design. Therefore, the feasibility, effectiveness, and safety of CAD for upper gastrointestinal endoscopy in clinical practice remain unknown, although a considerable number of pilot studies have been conducted by both engineers and medical doctors with excellent results. This review summarizes current publications relating to CAD for upper gastrointestinal endoscopy from the perspective of endoscopists and aims to indicate what is required for future research and implementation in clinical practice.

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Application of Convolutional Neural Networks in the Diagnosis of Helicobacter pylori Infection Based on Endoscopic Images

Cited by 246 publications

References 24 publications

Endoscopic Diagnostic Support System for cT1b Colorectal Cancer Using Deep Learning

Endoscopic Diagnostic Support System for cT1b Colorectal Cancer Using Deep Learning

Potential of automatic diagnosis system with linked color imaging for diagnosis of Helicobacter pylori infection

Artificial intelligence and upper gastrointestinal endoscopy: Current status and future perspective

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