Detection Of Gastritis Research Articles

Self-supervised learning for gastritis detection with gastric X-ray images.

Manual annotation of gastric X-ray images by doctors for gastritis detection is time-consuming and expensive. To solve this, a self-supervised learning method is developed in this study. The effectiveness of the proposed self-supervised learning method in gastritis detection is verified using a few annotated gastric X-ray images. In this study, we develop a novel method that can perform explicit self-supervised learning and learn discriminative representations from gastric X-ray images. Models trained based on the proposed method were fine-tuned on datasets comprising a few annotated gastric X-ray images. Five self-supervised learning methods, i.e., SimSiam, BYOL, PIRL-jigsaw, PIRL-rotation, and SimCLR, were compared with the proposed method. Furthermore, three previous methods, one pretrained on ImageNet, one trained from scratch, and one semi-supervised learning method, were compared with the proposed method. The proposed method's harmonic mean score of sensitivity and specificity after fine-tuning with the annotated data of 10, 20, 30, and 40 patients were 0.875, 0.911, 0.915, and 0.931, respectively. The proposed method outperformed all comparative methods, including the five self-supervised learning and three previous methods. Experimental results showed the effectiveness of the proposed method in gastritis detection using a few annotated gastric X-ray images. This paper proposes a novel self-supervised learning method based on a teacher-student architecture for gastritis detection using gastric X-ray images. The proposed method can perform explicit self-supervised learning and learn discriminative representations from gastric X-ray images. The proposed method exhibits potential clinical use in gastritis detection using a few annotated gastric X-ray images.

H. pylori Related Atrophic Gastritis Detection Using Enhanced Convolution Neural Network (CNN) Learner.

Atrophic gastritis (AG) is commonly caused by the infection of the Helicobacter pylori (H. pylori) bacteria. If untreated, AG may develop into a chronic condition leading to gastric cancer, which is deemed to be the third primary cause of cancer-related deaths worldwide. Precursory detection of AG is crucial to avoid such cases. This work focuses on H. pylori-associated infection located at the gastric antrum, where the classification is of binary classes of normal versus atrophic gastritis. Existing work developed the Deep Convolution Neural Network (DCNN) of GoogLeNet with 22 layers of the pre-trained model. Another study employed GoogLeNet based on the Inception Module, fast and robust fuzzy C-means (FRFCM), and simple linear iterative clustering (SLIC) superpixel algorithms to identify gastric disease. GoogLeNet with Caffe framework and ResNet-50 are machine learners that detect H. pylori infection. Nonetheless, the accuracy may become abundant as the network depth increases. An upgrade to the current standards method is highly anticipated to avoid untreated and inaccurate diagnoses that may lead to chronic AG. The proposed work incorporates improved techniques revolving within DCNN with pooling as pre-trained models and channel shuffle to assist streams of information across feature channels to ease the training of networks for deeper CNN. In addition, Canonical Correlation Analysis (CCA) feature fusion method and ReliefF feature selection approaches are intended to revamp the combined techniques. CCA models the relationship between the two data sets of significant features generated by pre-trained ShuffleNet. ReliefF reduces and selects essential features from CCA and is classified using the Generalized Additive Model (GAM). It is believed the extended work is justified with a 98.2% testing accuracy reading, thus providing an accurate diagnosis of normal versus atrophic gastritis.

Study on the Clinical Value of Serum Pepsinogen II in Gastritis Detection

The present study aims to explore the clinical value of pepsinogen II and gastritis detection. Firstly, serum expressions of pepsinogen II in patients with Helicobacter pylori-caused gastritis, gastric ulcer and atrophic gastritis were detected using enzyme-linked immunosorbent assay, followed by statistical analysis. In comparison with the normal group, serum pepsinogen II levels were increased in the above groups (p<0.01). Next, the positive cut-off value for pepsinogen II in detecting gastritis was obtained by the receiver operating characteristic curve. When the value of pepsinogen II was 10.55 μg/l, the specificity and sensitivity was 83.6 % and 74.4 % in the diagnosis of Helicobacter pylori-causing gastritis, 81.6 % and 80.0 % in gastric ulcer and 81.6 % and 53.1 % in atrophic gastritis. Thus, pepsinogen II at 10.55 μg/l was recommended as the positive cut-off value in gastritis detection. Besides, we observed that the ribonucleic acid and protein expressions of pepsinogen II in different gastritis groups were up-regulated by Western blotting and real-time polymerase chain reaction. Moreover, the immunohistochemistry indicated that pepsinogen II was highly expressed in gastritis tissues compared with normal controls. In conclusion, pepsinogen II could be an independent indicator in gastritis detection.

The role of endoscopic and demographic features in the diagnosis of gastric precancerous conditions.

The diagnosis of atrophic gastritis (AG) and intestinal metaplasia (IM) is a crucial screening and surveillance strategy for gastric adenocarcinoma. The main objective was to assess the performance of endoscopic diagnosis of gastric precancerous conditions in a real‑life scenario. A total of 2099 gastroscopies with biopsy to evaluate gastritis performed in 3 endoscopic centers from March 2018 to October 2019 were retrospectively analyzed. Endoscopic data regarding gastritis, atrophy, and intestinal metaplasia were compared with histopathological reports. The endoscopic diagnosis sensitivity was 69.5% for AG and 19.4% for IM. The specificity of endoscopic detection of AG was 69.5% and of IM, 97.9%. The endoscopic detection of gastritis was a risk factor for AG and IM diagnosis (odds ratio [OR], 5.1; 95% CI, 1.9-14.1 and OR, 14.5; 95% CI, 5.9-35.8, respectively) and the patient's age was a risk factor for AG, IM, dysplasia, and advanced stage of AG (ASAG) diagnosis (OR, 1.05; 95% CI, 1.04-1.06; OR, 1.035; 95% CI, 1.03-1.04; OR, 1.04; 95% CI, 1.02-1.06; and OR, 1.05; 95% CI, 1.02-1.09, respectively). The age threshold of 45 or 40 years with endoscopically diagnosed gastritis for obtaining biopsy would result in 96.3% and 95% ASAG or dysplasia diagnosis sensitivity, and in the reduction of the number of biopsies by 20.2% and 20.5%, respectively. The application of the age threshold with or without an endoscopic diagnosis of gastritis could reduce the number of mapping biopsies to detect advanced stages of atrophic gastritis or dysplasia with high sensitivity.

Helicobacter pylori Prevalence in Laparoscopic Sleeve Gastrectomy Specimen.

Introduction Laparoscopic sleeve gastrectomy (LSG) has become a common surgical procedure. The value of routine histopathologic examination of the LSG specimens remains, however, a controversial issue. Helicobacter pylori was the most prevalent finding in several previous studies, but the overall results were dissimilar. We aim to assess the prevalence of Helicobacter pylori and other histopathologic findings in LSG specimens and the effect of increasing the number of sections for histology, from LSG specimens, on the rates of abnormal findings. Methods We retrospectively reviewed the histopathologic data of all patients who had undergone LSG, in a tertiary care center, over a 4-year period (n = 481). Patient characteristics and histopathologic findings were recorded and analyzed. Results Inactive chronic gastritis was the most common histopathologic finding (62.16%) followed by Helicobacter pylori gastritis (35.34%). Intestinal metaplasia was identified in 1.66% of the cases. There was no diagnosis of malignancy. Increasing the number of sections submitted for histopathologic examination resulted in a significantly higher rate of H. pylori gastritis detection. Conclusion Routine histopathologic examination of LSG specimens may detect H. pylori in a significant proportion of patients, and increasing the number of sections for histology from LSG specimens improves the rate of detection of this bacterium and identifies individuals who may benefit from treatment.

Chronic atrophic gastritis detection with a convolutional neural network considering stomach regions.

BACKGROUNDThe risk of gastric cancer increases in patients with Helicobacter pylori-associated chronic atrophic gastritis (CAG). X-ray examination can evaluate the condition of the stomach, and it can be used for gastric cancer mass screening. However, skilled doctors for interpretation of X-ray examination are decreasing due to the diverse of inspections.AIMTo evaluate the effectiveness of stomach regions that are automatically estimated by a deep learning-based model for CAG detection.METHODSWe used 815 gastric X-ray images (GXIs) obtained from 815 subjects. The ground truth of this study was the diagnostic results in X-ray and endoscopic examinations. For a part of GXIs for training, the stomach regions are manually annotated. A model for automatic estimation of the stomach regions is trained with the GXIs. For the rest of them, the stomach regions are automatically estimated. Finally, a model for automatic CAG detection is trained with all GXIs for training.RESULTSIn the case that the stomach regions were manually annotated for only 10 GXIs and 30 GXIs, the harmonic mean of sensitivity and specificity of CAG detection were 0.955 ± 0.002 and 0.963 ± 0.004, respectively.CONCLUSIONBy estimating stomach regions automatically, our method contributes to the reduction of the workload of manual annotation and the accurate detection of the CAG.

Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography.

Deep learning has become a new trend of image recognition tasks in the field of medicine. We developed an automated gastritis detection system using double-contrast upper gastrointestinal barium X-ray radiography. A total of 6520 gastric X-ray images obtained from 815 subjects were analyzed. We designed a deep convolutional neural network (DCNN)-based gastritis detection scheme and evaluated the effectiveness of our method. The detection performance of our method was compared with that of ABC (D) stratification. Sensitivity, specificity, and harmonic mean of sensitivity and specificity of our method were 0.962, 0.983, and 0.972, respectively, and those of ABC (D) stratification were 0.925, 0.998, and 0.960, respectively. Although there were 18 false negative cases in ABC (D) stratification, 14 of those 18 cases were correctly classified into the positive group by our method. Deep learning techniques may be effective for evaluation of gastritis/non-gastritis. Collaborative use of DCNN-based gastritis detection systems and ABC (D) stratification will provide more reliable gastric cancer risk information.

Sa1721 Conventional Narrow Band Imaging for the Improved Detection of Gastritis

Sa1721 Conventional Narrow Band Imaging for the Improved Detection of Gastritis

Tu1559 Protein-Losing Enteropathy: Clinical Implication of Fluoroscopic Enteroclysis, Double-Balloon Endoscopy, and Videocapsule Endoscopy

patients and EGD in 23/30 (76.67%) patients. Regarding the number of lesions, CE could detect 14 additional lesions other than gastritis, the same number of lesions found by EGD that includes: 1 angyodisplasia, 2 adenomatous polyps, 6 gastric ulcers (being 1 an early gastric cancer), 3 intestinal metaplasia. Helicobacter pylori was present in 14/30 patients (46.66%) Biopsy from EGD revealed gastritis in 26/30 patients, being 25/26 active atrophic chronic gastritis and 1/26 active non atrophic chronic gastritis.Finally in this study the sensitivity and specificity of CE to detect gastritis was 88% and 66% respectively. Conclusion: In spite of the limitations of this study (number of patients and one tip CE), CE has a good sensitivity compared to EGD for the detection of gastritis and gastric lesions. CE could be a screening method to decide which patients should be submitted to Endoscopy with biopsy. In the future this indication could diminish the number of EGDs performed per year as well as the price of CE, which could have a good economic impact.

Tu1558 Capsule Endoscopy for Detection of Gastritis and Gastric Lesions in Dyspeptic Patients: A Pilot Study

Tu1558 Capsule Endoscopy for Detection of Gastritis and Gastric Lesions in Dyspeptic Patients: A Pilot Study

Campylobacter pylori - besteht ein Zusammenhang mit der peptischen Ulcuskrankheit?

In 67 patients, mucosal biopsies were taken in gastroduodenoscopy and culture set up to demonstrate Campylobacter pylori. Campylobacter pylori was cultured from 91% of the patients with peptic ulcer, in 76% of patients with gastritis and in 28% of patients without histological detection of gastritis. Electron microscopic investigations of duodenal mucosa showed that this bacterium attaches to the metaplastic cells of the antral type. This cell type is regularly encountered in the duodenal mucosa in healing duodenal ulcer. The ultrastructural features permit a clear distinction between Campylobacter pylori and other Campylobacter species. Campylobacter pylori may possibly have a pathogenic effect on the mucosa owing to its penetration into the interstitial spaces between the cells and into the interior of the cells.

Serum Pepsinogen Levels,Helicobacter pyloriCagA Status, and Cytokine Gene Polymorphisms Associated with Gastric Premalignant Lesions in Costa Rica

The detection of gastric premalignant lesions, atrophic gastritis, corpus atrophic gastritis, and intestinal metaplasia, using several potential markers was examined in Costa Rica. Depending on the lesion investigated, from a total of 223 dyspeptic patients, 58 (26.0%), 31 (13.9%), or 23 (10.3%) were histologically diagnosed with atrophic gastritis, corpus atrophic gastritis, or intestinal metaplasia, respectively. Sera were used for the measurement of pepsinogen (PG) and Helicobacter pylori CagA antibody (CagA-ab) levels by ELISA, and human genomic DNAs were used for the genotyping of interleukin (IL)-1beta (-511 and +3954), IL-10 (-1082 and -592), and IL-1RN intron 2 by PCR and RFLP. Multivariate analysis was done adjusting for sex, age, and H. pylori seropositivity. Low PG levels (L-PG; PG I < or = 70 microg/L + PG I/II < or = 3), very low PG levels (VL-PG; PG I < or = 30 microg/L + PG I/II < or = 2), and CagA-ab were individually associated with all premalignant lesions whereas IL-1beta +3954T-carrier and IL-1RN homozygous 2 allele were associated with intestinal metaplasia. VL-PG, for corpus atrophic gastritis detection, was the single marker with the highest combination of test characteristics, sensitivity (77.4%), specificity (80.7%), positive predictive value (39.3%), negative predictive value (95.7%), and seropositivity rate (27.4%), expected to improve after periodic measurements. Combined examinations of VL-PG and CagA-ab improved the specificity (92.7%) and positive predictive value (62.2%), with similar sensitivity (74.2%) and negative predictive value (95.7%). In conclusion, corpus atrophic gastritis detection with periodic measurements of serum PG, alone or in combination with CagA-ab status, to identify high gastric cancer risk, seems to be the method best suited for mass screening in Costa Rica.

Detection of gastritis by single- and double-contrast radiography.

Sixty-eight patients with various types of gastritis, 23 patients with normal stomachs, and four patients with other gastric diseases were examined in a prospective study to assess the sensitivity and specificity of single-contrast (SC) and double-contrast (DC) upper gastrointestinal examinations in the evaluation of gastritis. All patients underwent endoscopy with biopsy followed first by DC and then by SC radiography. The respective sensitivities of SC and DC radiography were 58% and 72% for all examinations and 59% and 77% for adequate examinations only. The respective specificities were 59% and 55% based on all examinations. Both methods were slightly more sensitive in the detection of moderate-to-severe disease, with sensitivities of 60% for SC and 74% for DC examinations as compared with sensitivities of 53% for SC and 70% for DC in the detection of mild gastritis. Useful radiographic features included polypoid defects and erosions detected by both methods, abnormal folds and flattened margins detected by the SC technique, and narrowed lumen and crenulated margins detected by the DC technique. In 93% of all cases, the correct diagnosis was based on two or more of these radiographic features. According to this study, the radiographic sensitivity in the detection of gastritis is reliable only in cases of moderate-to-severe disease and only when based on findings of the DC examination. Neither SC nor DC radiography should be used as the primary screening method for patients with suspected gastritis, and the radiographic diagnosis should be restricted to the terms "erosive" or "nonerosive gastritis."