Gallstone Frequency in Patients with Helicobacter pylori Gastritis

Background: Identification of the causes of gallstone would result in better planning for the prevention of this disease. One of the proposed risk factors for this problem is Helicobacter pylori(H. pylori) infection. Objectives: The purpose of this study was to determine the incidence rate of gallstone in patients with H. pylori gastritis. Methods: This was an observational study performed as a descriptive-comparative cross-sectional survey. We enrolled 169 consecutive patients with H. pylori gastritis admitted to Imam-Hossein Hospital, Tehran, Iran, in 2018, and gallstone frequency in them was determined and compared with other variables. Results: Overall, 14 (8.3%) patients had gallstone, and all the patients had H. pylori gastritis. There was no significant association between gallstone and H. pylori gastritis (P = 0.561). Conclusions: It may be concluded that gallstone frequency in patients with H. pylori gastritis is low, and there is no significant association between these two conditions.

A Deep Learning Convolutional Neural Network Can Differentiate Between Helicobacter Pylori Gastritis and Autoimmune Gastritis With Results Comparable to Gastrointestinal Pathologists

Context.— Pathology studies using convolutional neural networks (CNNs) have focused on neoplasms, while studies in inflammatory pathology are rare. We previously demonstrated a CNN differentiates reactive gastropathy, Helicobacter pylori gastritis (HPG), and normal gastric mucosa. Objective.— To determine whether a CNN can differentiate the following 2 gastric inflammatory patterns: autoimmune gastritis (AG) and HPG. Design.— Gold standard diagnoses were blindly established by 2 gastrointestinal (GI) pathologists. One hundred eighty-seven cases were scanned for analysis by HALO-AI. All levels and tissue fragments per slide were included for analysis. The cases were randomized, 112 (60%; 60 HPG, 52 AG) in the training set and 75 (40%; 40 HPG, 35 AG) in the test set. A HALO-AI correct area distribution (AD) cutoff of 50% or more was required to credit the CNN with the correct diagnosis. The test set was blindly reviewed by pathologists with different levels of GI pathology expertise as follows: 2 GI pathologists, 2 general surgical pathologists, and 2 residents. Each pathologist rendered their preferred diagnosis, HPG or AG. Results.— At the HALO-AI AD percentage cutoff of 50% or more, the CNN results were 100% concordant with the gold standard diagnoses. On average, autoimmune gastritis cases had 84.7% HALO-AI autoimmune gastritis AD and HP cases had 87.3% HALO-AI HP AD. The GI pathologists, general anatomic pathologists, and residents were on average, 100%, 86%, and 57% concordant with the gold standard diagnoses, respectively. Conclusions.— A CNN can distinguish between cases of HPG and autoimmune gastritis with accuracy equal to GI pathologists.