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

Autor: Franklin MM; From the Department of Pathology, University of New Mexico School of Medicine, Albuquerque. Hanson and Martin are co-senior authors on the manuscript., Schultz FA; From the Department of Pathology, University of New Mexico School of Medicine, Albuquerque. Hanson and Martin are co-senior authors on the manuscript., Tafoya MA; From the Department of Pathology, University of New Mexico School of Medicine, Albuquerque. Hanson and Martin are co-senior authors on the manuscript., Kerwin AA; From the Department of Pathology, University of New Mexico School of Medicine, Albuquerque. Hanson and Martin are co-senior authors on the manuscript., Broehm CJ; From the Department of Pathology, University of New Mexico School of Medicine, Albuquerque. Hanson and Martin are co-senior authors on the manuscript., Fischer EG; From the Department of Pathology, University of New Mexico School of Medicine, Albuquerque. Hanson and Martin are co-senior authors on the manuscript., Gullapalli RR; From the Department of Pathology, University of New Mexico School of Medicine, Albuquerque. Hanson and Martin are co-senior authors on the manuscript., Clark DP; From the Department of Pathology, University of New Mexico School of Medicine, Albuquerque. Hanson and Martin are co-senior authors on the manuscript., Hanson JA; From the Department of Pathology, University of New Mexico School of Medicine, Albuquerque. Hanson and Martin are co-senior authors on the manuscript., Martin DR; From the Department of Pathology, University of New Mexico School of Medicine, Albuquerque. Hanson and Martin are co-senior authors on the manuscript.
Jazyk: angličtina
Zdroj: Archives of pathology & laboratory medicine [Arch Pathol Lab Med] 2022 Jan 01; Vol. 146 (1), pp. 117-122.
DOI: 10.5858/arpa.2020-0520-OA
Abstrakt: Context.—: Pathology studies using convolutional neural networks (CNNs) have focused on neoplasms, while studies in inflammatory pathology are rare. We previously demonstrated a CNN that 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.
Databáze: MEDLINE
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