A Preliminary Study Assessing a Transfer Learning Approach to Intestinal Image Analysis to Help Determine Treatment Response in Canine Protein-Losing Enteropathy.

Simple Summary: Protein-losing enteropathy (PLE) in dogs is a condition resulting in loss of protein through the gastrointestinal tract. Protein-losing enteropathy has a guarded prognosis, with death occurring as a result of the condition in approximately 50% of dogs. This condition can be treated with diet alone or with immunosuppressant medication, and dogs treated with diet alone have a better long-term outcome. However, our ability to determine which dogs will respond to diet alone at diagnosis is limited. Therefore, the aim of our study was to determine if a machine transfer learning approach on images of intestinal biopsies collected via upper gastrointestinal tract endoscopy at diagnosis from dogs with PLE was able to predict their response to treatment. Our study showed that the model was able to differentiate intestinal biopsy images from dogs with food-responsive PLE (n = 7) from immunosuppressant-responsive PLE (n = 10) with an accuracy of 83.78%. Our results suggest that computational approaches at biopsy diagnosis may help to predict whether dogs with PLE will respond to food or immunosuppressant medication. This will help to ensure dogs with PLE are prescribed the most appropriate treatment at diagnosis to ensure optimal response and outcome. 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 (n = 7) or IR-PLE (n = 10) after 4 months of follow-up. A machine-based algorithm was used on lower magnification and higher resolution images of endoscopic duodenal biopsy specimens. Using the pre-trained Convolutional Neural Network model with a 70/30 training/test ratio for images, the model was able to differentiate endoscopic duodenal biopsy images from dogs with FR-PLE and IR-PLE with an accuracy of 83.78%. Our study represents an important first step toward the use of machine learning in improving the decision-making process for clinicians with regard to the initial treatment of canine PLE. [ABSTRACT FROM AUTHOR]

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