1. A Deep Learning Convolutional Neural Network Can Differentiate Between Helicobacter Pylori Gastritis and Autoimmune Gastritis With Results Comparable to Gastrointestinal Pathologists
- Author
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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., and Martin, David R.
- Subjects
Helicobacter infections -- Risk factors -- Prevention ,Artificial intelligence -- Usage ,Gastritis -- Diagnosis -- Care and treatment -- Risk factors ,Neural networks -- Analysis ,Neural network ,Artificial intelligence ,Health - Abstract
* 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., Deep learning is a new and emerging paradigm of artificial intelligence (AI) research. Convolutional neural networks (CNN) are an algorithmic form of deep learning that are highly accurate in image-based [...]
- Published
- 2022
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