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 that differentiates reactive gastropathy, Helicobacter pylori gastritis (HPG), and normal gastric mucosa. Objective.--T...
| Publicado en: | Archives of Pathology & Laboratory Medicine Vol. 146; no. 1; pp. 117 - 123 |
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| Autores principales: | , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
College of American Pathologists
Jan2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154443840&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154443840 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00039985 1FS jtl: Archives of Pathology & Laboratory Medicine issn: 00039985 maglogo: N pubinfo: dt: Jan2022 vid: 146 iid: 1 pid: 2550 pub: College of American Pathologists place: Northfield, Illinois artinfo: ui: 154443840 154443840 154443840 10.5858/arpa.2020-0520-OA 154443840 ppf: 117 ppct: 6 formats: fmt: @attributes: type: P tig: atl: A Deep Learning Convolutional Neural Network Can Differentiate Between Helicobacter Pylori Gastritis and Autoimmune Gastritis With Results Comparable to Gastrointestinal Pathologists. aug: au: Franklin, Michael M. Schultz, Fred A. Tafoya, Marissa A. Kerwin, Audra A. Broehm, Cory J. Fischer, Edgar G. Gullapalli, Rama R. Clark, Douglas P. Hanson, Joshua A. Martin, David R. affil: Department of Pathology, University of New Mexico School of Medicine, Albuquerque sug: subj: Deep Learning Neural Networks (Computer) Helicobacter Infections Diagnosis Gastritis Diagnosis Pathologists Autoimmune Diseases Diagnosis Diagnosis, Computer Assisted Human Tissue Analysis Interns and Residents Descriptive Statistics Comparative Studies Biopsy Image Processing, Computer Assisted Software ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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