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Proof of Concept for a Deep Learning Algorithm for Identification and Quantification of Key Microscopic Features in the Murine Model of DSS-Induced Colitis.
Bédard, Agathe; Westerling-Bui, Thomas; Zuraw, Aleksandra.
Afiliación
  • Bédard A; Pathology Department, 25913Charles River, Senneville, Quebec, Canada.
  • Westerling-Bui T; Aiforia Inc, Cambridge Innovation Center, Cambridge, MA, USA.
  • Zuraw A; Pathology Department, 25913Charles River, Senneville, Quebec, Canada.
Toxicol Pathol ; 49(4): 897-904, 2021 06.
Article en En | MEDLINE | ID: mdl-33576323
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Colitis / Aprendizaje Profundo Tipo de estudio: Diagnostic_studies / Prognostic_studies Aspecto: Patient_preference Límite: Animals Idioma: En Revista: Toxicol Pathol Año: 2021 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Colitis / Aprendizaje Profundo Tipo de estudio: Diagnostic_studies / Prognostic_studies Aspecto: Patient_preference Límite: Animals Idioma: En Revista: Toxicol Pathol Año: 2021 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos