Peer-reviewed veterinary case report
Using AI to Identify Dog Bladder Stones from X-Rays
By Canejo-Teixeira, Rute et al.·Published in Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association·2025·Department of Clinical Veterinary Medicine·View original on PubMed →
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Original publication title: A Pilot Study on Using an Artificial Intelligence Algorithm to Identify Urolith Composition through Abdominal Radiographs in the Dog.
- Species:
- dog
Plain-English summary
A group of 139 dogs with urinary stones (uroliths) underwent abdominal X-rays to see if an artificial intelligence app could accurately identify the type of stones present. The app, called CALCurad, was able to predict whether the stones were made of struvite with an accuracy of 81.3% when compared to traditional testing methods. This technology could help veterinarians decide whether to treat the stones with medication or surgery, making it easier to manage dogs with urinary issues. Overall, the study shows promise for using AI in veterinary care.
People also search for: dog urinary stones treatment · how to identify dog uroliths · struvite stones in dogs · dog X-ray for urinary problems
Abstract
In small animal practice, patients often present with urinary lithiasis, and prediction of urolith composition is essential to determine the appropriate treatment. Through abdominal radiographs, the composition of mineral radiopaque uroliths can be determined by considering many different factors; this can be complex and, as such, tailor-made for the use of artificial intelligence (AI). The Minnesota Urolith Center partnered with Hill's Pet Nutrition to develop a deep learning AI algorithm (CALCurad) within a smartphone application called the MN Urolith Application that allows for the preliminary assessment of urolith composition. The algorithm provides the probability of a urolith being composed of struvite from an image taken of an abdominal radiograph. This pilot study evaluates the accuracy of the CALCurad in the context of clinical practice. A sample population of 139 dogs was considered, and the results obtained by the CALCurad were compared with the results obtained by infrared spectroscopy analysis. Agreement between the application and quantitative analyses was 81.3%. These results suggest that the CALCurad can effectively be used to predict urolith composition in dogs, helping the clinician to decide between medical and surgical management of the patient. The use of the CALCurad is an example of the usefulness of AI in helping veterinarians make clinical decisions in patient care.
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Search related cases →Original publication on PubMed: https://pubmed.ncbi.nlm.nih.gov/40065198/