Proof of Concept for a Deep Learning Algorithm for Identification and Quantification of Key Microscopic Features in the Murine Model of DSS-Induced Colitis
| Autor: | Aleksandra Zuraw, Agathe Bédard, Thomas Westerling-Bui |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Pathology
medicine.medical_specialty Colon 040301 veterinary sciences Inflammation Toxicology 030226 pharmacology & pharmacy Convolutional neural network Inflammatory bowel disease Pathology and Forensic Medicine 0403 veterinary science Mice 03 medical and health sciences Deep Learning 0302 clinical medicine Artificial Intelligence Animals Medicine Colitis Molecular Biology Entire colon business.industry Deep learning Dextran Sulfate Digital pathology 04 agricultural and veterinary sciences Cell Biology medicine.disease digestive system diseases Mice Inbred C57BL Disease Models Animal Murine model Quality of Life Artificial intelligence medicine.symptom business |
| Zdroj: | Toxicologic Pathology. 49:897-904 |
| ISSN: | 1533-1601 0192-6233 |
| DOI: | 10.1177/0192623320987804 |
| Popis: | Inflammatory bowel disease (IBD) is a complex disease which leads to life-threatening complications and decreased quality of life. The dextran sulfate sodium (DSS) colitis model in mice is known for rapid screening of candidate compounds. Efficacy assessment in this model relies partly on microscopic semiquantitative scoring, which is time-consuming and subjective. We hypothesized that deep learning artificial intelligence (AI) could be used to identify acute inflammation in H&E-stained sections in a consistent and quantitative manner. Training sets were established using ×20 whole slide images of the entire colon. Supervised training of a Convolutional Neural Network (CNN) was performed using a commercial AI platform to detect the entire colon tissue, the muscle and mucosa layers, and 2 categories within the mucosa (normal and acute inflammation E1). The training sets included slides of naive, vehicle-DSS and cyclosporine A-DSS mice. The trained CNN was able to segment, with a high level of concordance, the different tissue compartments in the 3 groups of mice. The segmented areas were used to determine the ratio of E1-affected mucosa to total mucosa. This proof-of-concept work shows promise to increase efficiency and decrease variability of microscopic scoring of DSS colitis when screening candidate compounds for IBD. |
| Databáze: | OpenAIRE |
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