BRIEF RESEARCH REPORT article
Front. Artif. Intell.
Sec. Medicine and Public Health
This article is part of the Research TopicTransforming Veterinary Medicine: Digital Tools and AI as Path to Sustainable Animal CareView all 8 articles
Detection of Protein-Losing Enteropathy (PLE) Ultrasonographic Imaging Features in Dogs Using Deep Learning Neural Networks
Provisionally accepted
- 1Department for Small Animals, College of Veterinary Medicine, University of Leipzig, Leipzig, Germany
- 2Department of Computer Science and Software Engineering, Lancaster University Leipzig, Leipzig, Germany
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Artificial intelligence (AI)-based models and algorithms may aid in achieving overall more efficient and accurate diagnostics in various medical specialties. Such AI-based tools could be integrated and potentially offer advantages over currently used diagnostic and monitoring algorithms, enabling the pursue of more individualized treatment options with potentially improved patient outcomes in the future. However, very few studies exploring the potential of AI-based tools have been reported in veterinary medicine. Diagnosis and subclassification of chronic inflammatory enteropathy (CIE) and protein-losing enteropathy (PLE), requiring an integrated approach including several diagnostic modalities, remains a challenge in clinical canine gastroenterology and might benefit from AI-based tools. Thus, we aimed to use AI-based deep learning to develop a model that can differentiate clinical cases of protein-losing PLE from non-PLE CIE using ultrasonographic (B-mode) images. This pilot study included anonymized data extracted from the electronic medical records and diagnostic images from routine diagnostic evaluations of 59 dogs. Following several optimization steps, the final model had a high accuracy (91.57%), precision (0.9286), recall (0.9070), F1 score (0.9176), and AUC-ROC (0.9529). This model was highly sensitive and specific for the detection of ultrasonographic features associated with clinicopathologic and/or histological lesions consistent with a PLE diagnosis. Combining sonographic diagnostics with machine learning yielded a high degree of accuracy in PLE differentiation. The results of this study underscore the potential of integrating an AI-based model into CIE diagnostics and PLE differentiation in clinical canine gastroenterology.
Keywords: artificial intelligence, canine, chronic inflammatory enteropathy, deep learning, diagnosis, machine learning, Model, Resnet
Received: 19 Sep 2025; Accepted: 16 Dec 2025.
Copyright: © 2025 Reichert, Ali, Asif and Heilmann. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Romy Monika Heilmann
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