A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology

Authors

David R. Martin, University of New Mexico, Departments of Pathology
Joshua A. Hanson, University of New Mexico, Departments of Pathology
Rama R. Gullapalli, University of New Mexico, Departments of Pathology; Department of Chemical and Biological Engineering
Fred A. Schultz, University of New Mexico, Departments of Pathology
Aisha Sethi, University of New Mexico, Departments of Pathology
Douglas P. Clark, University of New Mexico, Departments of Pathology

Document Type

Article

Publication Date

3-1-2020

Abstract

CONTEXT.—: Most deep learning (DL) studies have focused on neoplastic pathology, with the realm of inflammatory pathology remaining largely untouched.

OBJECTIVE.—: To investigate the use of DL for nonneoplastic gastric biopsies.

DESIGN.—: Gold standard diagnoses were blindly established by 2 gastrointestinal pathologists. For phase 1, 300 classic cases (100 normal, 100

RESULTS.—: For Phase 1, receiver operating curves showed near perfect agreement with the gold standard diagnoses at an AD percentage cutoff of 50% for normal (area under the curve [AUC] = 99.7%) and

CONCLUSIONS.—: A convolutional neural network can serve as an effective screening tool/diagnostic aid for

Recommended Citation

Martin DR, Hanson JA, Gullapalli RR, Schultz FA, Sethi A, Clark DP. A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology. Arch Pathol Lab Med. 2020 Mar;144(3):370-378. doi: 10.5858/arpa.2019-0004-OA. Epub 2019 Jun 27. PMID: 31246112.