Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal endoscopy images

Itoh, Takahiko; Kawahira, Hiroshi; Nakashima, Hirotaka; Yata, Noriko

doi:10.1055/s-0043-120830

Cited by 160 publications

(127 citation statements)

References 27 publications

Supporting

Mentioning

122

Contrasting

Unclassified

Order By: Relevance

“…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%

“…In recent years, we have tried to introduce the convolutional neural network (CNN), known as deep learning, to endoscopic diagnosis, which has been attracting attention in the image recognition technology field by using popular machine learning processes [17]. Machine learning is a method in which the computer is taught to find helpful regularities based on data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Endoscopic Diagnostic Support System for cT1b Colorectal Cancer Using Deep Learning

Ito

Kawahira

Nakashima³

et al. 2018

Oncology

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 54%

Section: Introductionmentioning

confidence: 99%

Endoscopic Diagnostic Support System for cT1b Colorectal Cancer Using Deep Learning

Ito

Kawahira

Nakashima³

et al. 2018

Oncology

Self Cite

View full text Add to dashboard Cite

show abstract

“…Helicobacter pylori ( HP )‐associated chronic gastritis can cause mucosal atrophy and intestinal metaplasia, both of which increase the risk of gastric cancer . Gastroscopic examination is very helpful to detect HP infection at an early stage.…”

Section: Stomachmentioning

confidence: 99%

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

Mori

Kudo

Mohmed

et al. 2019

Digestive Endoscopy

105

View full text Add to dashboard Cite

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.

show abstract

“…In another study, Itoh et al developed a convolutional neural network which is a machine‐learning algorithm capable of recognizing specific features of gastric endoscopy images to detect H. pylori infection earlier. Both the sensitivity and specificity for detection of H. pylori infection were 86.7% …”

Section: Endoscopic Diagnosismentioning

confidence: 99%

Diagnosis of Helicobacter pylori infection

2018

View full text Add to dashboard Cite

The progress this year in Helicobacter pylori diagnosis concerned essentially endoscopy and molecular techniques. New endoscopy techniques such as blue laser imaging and magnifying narrow band imaging allow the visualization of mucosal aspects representing H. pylori infection, intestinal metaplasia, and even ambiguous early gastric cancer. Several real-time PCRs have also been used either to quantify H. pylori or to detect mutations associated with clarithromycin resistance in gastric biopsies or applied on gastric juice, stool specimens, or the oral cavity. The presence of H. pylori in free-living amebae purified from wastewater and drinking water was also determined by PCR and sequencing, as well as culture from a few wastewater samples. Among the noninvasive methods, the urea breath test was used in different conditions, including with a new test meal, which is claimed to avoid the proton-pump inhibitor washout period before testing. Several articles concerning antibody detection and stool antigen test were also published.

show abstract

Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal endoscopy images

Cited by 160 publications

References 27 publications

Endoscopic Diagnostic Support System for cT1b Colorectal Cancer Using Deep Learning

Endoscopic Diagnostic Support System for cT1b Colorectal Cancer Using Deep Learning

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

Diagnosis of Helicobacter pylori infection

Contact Info

Product

Resources

About