Development and technical validation of an artificial intelligence model for quantitative analysis of histopathologic features of eosinophilic esophagitis

Development and technical validation of an artificial intelligence model for quantitative analysis of histopathologic features of eosinophilic esophagitis

Background: In an attempt to provide quantitative, reproducible, and standardized analyses in cases of eosinophilic esophagitis (EoE), we have developed an artificial intelligence (AI) digital pathology model for the evaluation of histologic features in the EoE/esophageal eosinophilia spectrum. Here...

Full description

Bibliographic Details
Main Authors: Luisa Ricaurte Archila, Lindsey Smith, Hanna-Kaisa Sihvo, Thomas Westerling-Bui, Ville Koponen, Donnchadh M. O’Sullivan, Maria Camila Cardenas Fernandez, Erin E. Alexander, Yaohong Wang, Priyadharshini Sivasubramaniam, Ameya Patil, Puanani E. Hopson, Imad Absah, Karthik Ravi, Taofic Mounajjed, Rish Pai, Catherine Hagen, Christopher Hartley, Rondell P. Graham, Roger K. Moreira
Format: Article
Language:English
Published: Elsevier 2022-01-01
Series:Journal of Pathology Informatics
Subjects:
Artificial intelligence
Digital pathology
Eosinophilic esophagitis
Deep learning
EoE
Eosinophils
Online Access:http://www.sciencedirect.com/science/article/pii/S2153353922007386

Internet

http://www.sciencedirect.com/science/article/pii/S2153353922007386

Similar Items