ArticleScientific reports2021
Machine-learning based prediction of Cushing's syndrome in dogs attending UK primary-care veterinary practice.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 26 citations in OpenAlex.
- Artificial intelligence and laboratory biomarkers in veterinary medicine: an update about machine learning applications.The veterinary quarterly · 2026Review
- Machine learning-assisted screening for canine Cushing's syndrome.The veterinary quarterly · 2026Article
- The Expanding Role of Artificial Intelligence in Companion Animal Care: A Systematic Review.Animals : an open access journal from MDPI · 2026Review
- The influence, promise, and potential perils of artificial intelligence in veterinary medicine: a call for improved awareness and literacy.Journal of veterinary internal medicine · 2026Article
- Artificial Intelligence in Veterinary Clinical Pathology-An Introduction and Review.Veterinary clinical pathology · 2025Review
- Classification of Clinical Outcomes in Hospitalized Asian Elephants Using Machine Learning and Survival Analysis: A Retrospective Study (2019-2024).Veterinary sciences · 2025Article
- Anna: an open-source platform for real-time integration of machine learning classifiers with veterinary electronic health records.BMC veterinary research · 2025Article
- Machine-learning prediction of postoperative complications after high tibial osteotomy for canine cranial cruciate ligament disease.Veterinary surgery : VS · 2025Article
- Machine-learning-based prediction of functional recovery in deep-pain-negative dogs after decompressive thoracolumbar hemilaminectomy for acute intervertebral disc extrusion.Veterinary surgery : VS · 2025Article
- Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.Veterinary medicine and science · 2025Review
- Construction of a Diagnostic Prediction Model for Feline Nasal and Nasopharyngeal Diseases in Japan Using Noninvasive Examinations.Veterinary medicine and science · 2025Article
- Use of Saliva Analytes as a Predictive Model to Detect Diseases in the Pig: A Pilot Study.Metabolites · 2025Article
- Development of machine learning models to predict clinical outcome and recovery time in dogs with parvovirus enteritis.Frontiers in veterinary science · 2025Article
- Predicting health outcomes in dogs using insurance claims data.Scientific reports · 2023Article
- Machine learning-based risk prediction model for canine myxomatous mitral valve disease using electronic health record data.Frontiers in veterinary science · 2023Article
- Diagnostic Classification of Cases of Canine Leishmaniasis Using Machine Learning.Sensors (Basel, Switzerland) · 2022Article
- Metabolomic Abnormalities in Serum from Untreated and Treated Dogs with Hyper- and Hypoadrenocorticism.Metabolites · 2022Article
- Identification of parameters and formulation of a statistical and machine learning model to identify Babesia canis infections in dogs using available ADVIA hematology analyzer data.Parasites & vectors · 2022Article
- External validation of a United Kingdom primary-care Cushing's prediction tool in a population of referred Dutch dogs.Journal of veterinary internal medicineArticle
Corrections and comments
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Authors and funding
7 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cushing's syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing's syndrome can be a challenging diagnosis to confirm, therefore new methods to aid diagnosis are warranted. Four machine-learning algorithms were applied to predict a future diagnosis of Cushing's syndrome, using structured clinical data from the VetCompass programme in the UK. Dogs suspected of having Cushing's syndrome were included in the analysis and classified based on their final reported diagnosis within their clinical records. Demographic and clinical features available at the point of first suspicion by the attending veterinarian were included within the models. The machine-learning methods were able to classify the recorded Cushing's syndrome diagnoses, with good predictive performance. The LASSO penalised regression model indicated the best overall performance when applied to the test set with an AUROC = 0.85 (95% CI 0.80-0.89), sensitivity = 0.71, specificity = 0.82, PPV = 0.75 and NPV = 0.78. The findings of our study indicate that machine-learning methods could predict the future diagnosis of a practicing veterinarian. New approaches using these methods could support clinical decision-making and contribute to improved diagnosis of Cushing's syndrome in dogs.
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Registered trials
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