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

Researchers from the University of New Mexico set out to investigate tissue classification using deep learning to evaluate nonneoplastic gastric biopsies. Ground truth diagnosis was established by gastrointestinal pathologists. HALO AI was trained to recognize Helicobacter pylori (H pylori) mediated gastritis, chemical gastropathy, normal mucosa, smooth muscle, and glass. The HALO AI classifier showed high sensitivity and specificity for control biopsies and gastropathy cases and represents the first deep learning driven evaluation of inflammatory gastrointestinal pathology published. The sensitivity and specificity was as follows: normal tissue (73.7% and 79.6%), H pylori (95.7% and 100%), and reactive gastropathy (100% and 62.5%). Martin and colleagues conclude that a convolutional neural network such as HALO AI can function as a screening aid for H pylori gastritis.

Martin, D.R., Hanson, J.A., Gullapalli, R.R., Schultz, F.A., Sethi, A. and Clark, D.P.

Archives of Pathology & Laboratory Medicine In-Press | First published 05 April 2020 | DOI https://doi.org/10.5858/arpa.2019-0004-OA