ArticleVeterinary sciences2024
A Preliminary Study Assessing a Transfer Learning Approach to Intestinal Image Analysis to Help Determine Treatment Response in Canine Protein-Losing Enteropathy.
Article in Veterinary sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Detection of protein-losing enteropathy (PLE) ultrasonographic imaging features in dogs using deep learning neural networks.Frontiers in artificial intelligence · 2025Article
- Deep-learning based morphological segmentation of canine diffuse large B-cell lymphoma.Frontiers in veterinary science · 2025Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Dogs with protein-losing enteropathy (PLE) caused by inflammatory enteritis, intestinal lymphangiectasia, or both, have a guarded prognosis, with death occurring as a result of the disease in approximately 50% of cases. Although dietary therapy alone is significantly associated with a positive outcome, there is limited ability to differentiate between food-responsive (FR) PLE and immunosuppressant-responsive (IR) PLE at diagnosis in dogs. Our objective was to determine if a transfer learning computational approach to image classification on duodenal biopsy specimens collected at diagnosis was able to differentiate FR-PLE from IR-PLE. This was a retrospective study using paraffin-embedded formalin-fixed duodenal biopsy specimens collected during upper gastrointestinal tract endoscopy as part of the diagnostic investigations from 17 client-owned dogs with PLE due to inflammatory enteritis at a referral teaching hospital that were subsequently classified based on treatment response into FR-PLE (
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