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Helicobacter pylori Biofilm Formation and Its Potential Role in Pathogenesis. Hathroubi Skander,Servetas Stephanie L,Windham Ian,Merrell D Scott,Ottemann Karen M Microbiology and molecular biology reviews : MMBR Despite decades of effort, infections remain difficult to treat. Over half of the world's population is infected by , which is a major cause of duodenal and gastric ulcers as well as gastric cancer. During chronic infection, localizes within the gastric mucosal layer, including deep within invaginations called glands; thanks to its impressive ability to survive despite the harsh acidic environment, it can persist for the host's lifetime. This ability to survive and persist in the stomach is associated with urease production, chemotactic motility, and the ability to adapt to the fluctuating environment. Additionally, biofilm formation has recently been suggested to play a role in colonization. Biofilms are surface-associated communities of bacteria that are embedded in a hydrated matrix of extracellular polymeric substances. Biofilms pose a substantial health risk and are key contributors to many chronic and recurrent infections. This link between biofilm-associated bacteria and chronic infections likely results from an increased tolerance to conventional antibiotic treatments as well as immune system action. The role of this biofilm mode in antimicrobial treatment failure and survival has yet to be determined. Furthermore, relatively little is known about the biofilm structure or the genes associated with this mode of growth. In this review, therefore, we aim to highlight recent findings concerning biofilms and the molecular mechanism of their formation. Additionally, we discuss the potential roles of biofilms in the failure of antibiotic treatment and in infection recurrence. 10.1128/MMBR.00001-18
Potential of automatic diagnosis system with linked color imaging for diagnosis of Helicobacter pylori infection. Yasuda Takeshi,Hiroyasu Tomoyuki,Hiwa Satoru,Okada Yuto,Hayashi Sadanari,Nakahata Yuki,Yasuda Yuriko,Omatsu Tatsushi,Obora Akihiro,Kojima Takao,Ichikawa Hiroshi,Yagi Nobuaki Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society 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. 10.1111/den.13509
Review: Diagnosis of Helicobacter pylori infection. Makristathis Athanasios,Hirschl Alexander M,Mégraud Francis,Bessède Emilie Helicobacter Endoscopic imaging of the stomach is improving. In addition to narrow band imaging, other methods, for example, blue light imaging and linked color imaging, are now available and can be combined with artificial intelligence systems to obtain information on the gastric mucosa and detect early gastric cancer. Immunohistochemistry is only recommended as an ancillary stain in case of chronic active gastritis without Helicobacter pylori detection by standard staining, and recommendations to exclude false negative H. pylori results have been made. Molecular methods using real-time PCR, droplet digital PCR, or amplification refractory mutation system PCR have shown a high accuracy, both for detecting H. pylori and for clarithromycin susceptibility testing, and can now be used in clinical practice for targeted therapy. The most reliable non-invasive test remains the C-urea breath test. Large data sets show that DOB values are higher in women and that the cut-off for positivity could be decreased to 2.74 DOB. Stool antigen tests using monoclonal antibodies are widely used and may be a good alternative to UBT, particularly in countries with a high prevalence of H. pylori infection. Attempts to improve serology by looking at specific immunodominant antigens to distinguish current and past infection have been made. The interest of Gastropanel which also tests pepsinogen levels was confirmed. 10.1111/hel.12641
High Accuracy of Convolutional Neural Network for Evaluation of Helicobacter pylori Infection Based on Endoscopic Images: Preliminary Experience. Clinical and translational gastroenterology OBJECTIVES:Application of artificial intelligence in gastrointestinal endoscopy is increasing. The aim of the study was to examine the accuracy of convolutional neural network (CNN) using endoscopic images for evaluating Helicobacter pylori (H. pylori) infection. METHODS:Patients who received upper endoscopy and gastric biopsies at Sir Run Run Shaw Hospital (January 2015-June 2015) were retrospectively searched. A novel Computer-Aided Decision Support System that incorporates CNN model (ResNet-50) based on endoscopic gastric images was developed to evaluate for H. pylori infection. Diagnostic accuracy was evaluated in an independent validation cohort. H. pylori infection was defined by the presence of H. pylori on immunohistochemistry testing on gastric biopsies and/or a positive 13C-urea breath test. RESULTS:Of 1,959 patients, 1,507 (77%) including 847 (56%) with H. pylori infection (11,729 gastric images) were assigned to the derivation cohort, and 452 (23%) including 310 (69%) with H. pylori infection (3,755 images) were assigned to the validation cohort. The area under the curve for a single gastric image was 0.93 (95% confidence interval [CI] 0.92-0.94) with sensitivity, specificity, and accuracy of 81.4% (95% CI 79.8%-82.9%), 90.1% (95% CI 88.4%-91.7%), and 84.5% (95% CI 83.3%-85.7%), respectively, using an optimal cutoff value of 0.3. Area under the curve for multiple gastric images (8.3 ± 3.3) per patient was 0.97 (95% CI 0.96-0.99) with sensitivity, specificity, and accuracy of 91.6% (95% CI 88.0%-94.4%), 98.6% (95% CI 95.0%-99.8%), and 93.8% (95% CI 91.2%-95.8%), respectively, using an optimal cutoff value of 0.4. DISCUSSION:In this pilot study, CNN using multiple archived gastric images achieved high diagnostic accuracy for the evaluation of H. pylori infection. 10.14309/ctg.0000000000000109
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 Takahide,Takiyama Hirotoshi,Tanimoto Tetsuya,Ishihara Soichiro,Matsuo Keigo,Tada Tomohiro EBioMedicine BACKGROUND AND AIMS:The role of artificial intelligence in the diagnosis of Helicobacter pylori gastritis based on endoscopic images has not been evaluated. We constructed a convolutional neural network (CNN), and evaluated its ability to diagnose H. pylori infection. METHODS:A 22-layer, deep CNN was pre-trained and fine-tuned on a dataset of 32,208 images either positive or negative for H. pylori (first CNN). Another CNN was trained using images classified according to 8 anatomical locations (secondary CNN). A separate test data set (11,481 images from 397 patients) was evaluated by the CNN, and 23 endoscopists, independently. RESULTS:The sensitivity, specificity, accuracy, and diagnostic time were 81.9%, 83.4%, 83.1%, and 198s, respectively, for the first CNN, and 88.9%, 87.4%, 87.7%, and 194s, respectively, for the secondary CNN. These values for the 23 endoscopists were 79.0%, 83.2%, 82.4%, and 230±65min (85.2%, 89.3%, 88.6%, and 253±92min by 6 board-certified endoscopists), respectively. The secondary CNN had a significantly higher accuracy than endoscopists (by 5.3%; 95% CI, 0.3-10.2). CONCLUSION:H. pylori gastritis could be diagnosed based on endoscopic images using CNN with higher accuracy and in a considerably shorter time compared to manual diagnosis by endoscopists. 10.1016/j.ebiom.2017.10.014
Application of convolutional neural networks for evaluating Helicobacter pylori infection status on the basis of endoscopic images. Shichijo Satoki,Endo Yuma,Aoyama Kazuharu,Takeuchi Yoshinori,Ozawa Tsuyoshi,Takiyama Hirotoshi,Matsuo Keigo,Fujishiro Mitsuhiro,Ishihara Soichiro,Ishihara Ryu,Tada Tomohiro Scandinavian journal of gastroenterology BACKGROUND AND AIM:We recently reported the role of artificial intelligence in the diagnosis of Helicobacter pylori (H. pylori) gastritis on the basis of endoscopic images. However, that study included only H. pylori-positive and -negative patients, excluding patients after H. pylori-eradication. In this study, we constructed a convolutional neural network (CNN) and evaluated its ability to ascertain all H. pylori infection statuses. METHODS:A deep CNN was pre-trained and fine-tuned on a dataset of 98,564 endoscopic images from 5236 patients (742 H. pylori-positive, 3649 -negative, and 845 -eradicated). A separate test data set (23,699 images from 847 patients; 70 positive, 493 negative, and 284 eradicated) was evaluated by the CNN. RESULTS:The trained CNN outputs a continuous number between 0 and 1 as the probability index for H. pylori infection status per image (Pp, H. pylori-positive; Pn, negative; Pe, eradicated). The most probable (largest number) of the three infectious statuses was selected as the 'CNN diagnosis'. Among 23,699 images, the CNN diagnosed 418 images as positive, 23,034 as negative, and 247 as eradicated. Because of the large number of H. pylori negative findings, the probability of H. pylori-negative was artificially re-defined as Pn -0.9, after which 80% (465/582) of negative diagnoses were accurate, 84% (147/174) eradicated, and 48% (44/91) positive. The time needed to diagnose 23,699 images was 261 seconds. CONCLUSION:We used a novel algorithm to construct a CNN for diagnosing H. pylori infection status on the basis of endoscopic images very quickly. ABBREVIATIONS:H. pylori: Helicobacter pylori; CNN: convolutional neural network; AI: artificial intelligence; EGD: esophagogastroduodenoscopies. 10.1080/00365521.2019.1577486
Artificial intelligence diagnosis of infection using blue laser imaging-bright and linked color imaging: a single-center prospective study. Nakashima Hirotaka,Kawahira Hiroshi,Kawachi Hiroshi,Sakaki Nobuhiro Annals of gastroenterology BACKGROUND:Deep learning is a type of artificial intelligence (AI) that imitates the neural network in the brain. We generated an AI to diagnose () infection using blue laser imaging (BLI)-bright and linked color imaging (LCI). The aim of this pilot study was to establish an AI diagnosing system that predicts infection status using endoscopic images to improve the accuracy and productivity of endoscopic examination. METHODS:A total of 222 enrolled subjects (105 -positive) underwent esophagogastroduodenoscopy and a serum test for IgG antibodies. During esophagogastroduodenoscopy, an endoscopist sequentially took 3 still images of the lesser curvature of the stomach using white light imaging (WLI), BLI-bright, and LCI. EG-L580NW endoscopic equipment (FUJIFILM Co., Japan) was used for the study. The specifications of the AI were as follows: operating system, Linux; neural network, GoogLeNet; framework, Caffe; graphic processor unit, Geforce GTX TITAN X (NVIDIA Co., USA). RESULTS:The area under the curve (AUC) on receiver operating characteristics analysis was 0.66 for WLI. In contrast, the AUCs of BLI-bright and LCI were 0.96 and 0.95, respectively. The AUCs obtained for BLI-bright and LCI were significantly larger than those for WLI (P<0.01). CONCLUSIONS:The results demonstrate that the developed AI has an excellent ability to diagnose infection using BLI-bright and LCI. AI technology with image-enhanced endoscopy is likely to become a useful image diagnostic tool. 10.20524/aog.2018.0269