1. A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology.
- Author
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Martin, David R., Hanson, Joshua A., Gullapalli, Rama R., Schultz, Fred A., Sethi, Aisha, and Clark, Douglas P.
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HELICOBACTER disease diagnosis , *ALGORITHMS , *BIOPSY , *COMPUTER software , *GASTRITIS , *ARTIFICIAL neural networks , *PREDICTIVE tests , *RECEIVER operating characteristic curves , *DESCRIPTIVE statistics , *DEEP learning - 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 Helicobacter pylori, 100 reactive gastropathy) that best displayed the desired pathology were scanned and annotated for DL analysis. A total of 70% of the cases for each group were selected for the training set, and 30% were included in the test set. The software assigned colored labels to the test biopsies, which corresponded to the area of the tissue assigned a diagnosis by the DL algorithm, termed area distribution (AD). For Phase 2, an additional 106 consecutive nonclassical gastric biopsies from our archives were tested in the same fashion. 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 H pylori (AUC = 100%), and 40% for reactive gastropathy (AUC = 99.9%). Sensitivity/specificity pairings were as follows: normal (96.7%, 86.7%), H pylori (100%, 98.3%), and reactive gastropathy (96.7%, 96.7%). For phase 2, receiver operating curves were slightly less discriminatory, with optimal AD cutoffs reduced to 40% across diagnostic groups. The AUCs were 91.9% for normal, 100% for H pylori, and 94.0% for reactive gastropathy. Sensitivity/ specificity parings were as follows: normal (73.7%, 79.6%), H pylori (95.7%, 100%), reactive gastropathy (100%, 62.5%). Conclusions.--A convolutional neural network can serve as an effective screening tool/diagnostic aid for H pylori gastritis. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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