ArticleJournal of veterinary internal medicine2019
Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning.
Article in Journal of veterinary internal medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Review of applications of deep learning in veterinary diagnostics and animal health.Frontiers in veterinary science · 2025Pooled it
- Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis.Renal failure · 2024Pooled it
- 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
- Enhancing Detection of Feline Chronic Kidney Disease Through Smart Litter Box Monitoring.Animals : an open access journal from MDPI · 2026Article
- Bridging Species with AI: A Cross-Species Deep Learning Model for Fracture Detection and Beyond.Bioengineering (Basel, Switzerland) · 2026Article
- Applying Supervised Machine Learning to Effusion Analysis for the Diagnosis of Feline Infectious Peritonitis.Bioengineering (Basel, Switzerland) · 2026Article
- 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
- Predicting stage B2 myxomatous mitral valve disease in dogs using machine learning and routine clinical data.Frontiers in veterinary science · 2026Article
- Integration of machine learning and viscoelastic testing to improve survival prediction in horses experiencing acute abdominal pain at a veterinary teaching hospital.Equine veterinary journal · 2026Article
- Artificial Intelligence in Veterinary Clinical Pathology-An Introduction and Review.Veterinary clinical pathology · 2025Review
- Development of criteria to optimize manual smear review of automated complete blood counts using a machine learning model.Veterinary clinical pathology · 2025Article
- Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.Veterinary medicine and science · 2025Review
- Early detection of feline chronic kidney disease via 3-hydroxykynurenine and machine learning.Scientific reports · 2025Article
- Untargeted metabolomic profiling of serum from client-owned cats with early and late-stage chronic kidney disease.Scientific reports · 2024Article
- Artificial Intelligence in Kidney Disease: A Comprehensive Study and Directions for Future Research.Diagnostics (Basel, Switzerland) · 2024Review
- Assessing the feasibility of applying machine learning to diagnosing non-effusive feline infectious peritonitis.Scientific reports · 2024Article
- The MARS PETCARE BIOBANK protocol: establishing a longitudinal study of health and disease in dogs and cats.BMC veterinary research · 2023Article
- Development and internal validation of diagnostic prediction models using machine-learning algorithms in dogs with hypothyroidism.Frontiers in veterinary science · 2023Article
- Machine learning-based risk prediction model for canine myxomatous mitral valve disease using electronic health record data.Frontiers in veterinary science · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundAdvanced machine learning methods combined with large sets of health screening data provide opportunities for diagnostic value in human and veterinary medicine. HYPOTHESIS/
objectivesTo derive a model to predict the risk of cats developing chronic kidney disease (CKD) using data from electronic health records (EHRs) collected during routine veterinary practice. ANIMALS: A total of 106 251 cats that attended Banfield Pet Hospitals between January 1, 1995, and December 31, 2017.
methodsLongitudinal EHRs from Banfield Pet Hospitals were extracted and randomly split into 2 parts. The first 67% of the data were used to build a prediction model, which included feature selection and identification of the optimal neural network type and architecture. The remaining unseen EHRs were used to evaluate the model performance.
resultsThe final model was a recurrent neural network (RNN) with 4 features (creatinine, blood urea nitrogen, urine specific gravity, and age). When predicting CKD near the point of diagnosis, the model displayed a sensitivity of 90.7% and a specificity of 98.9%. Model sensitivity decreased when predicting the risk of CKD with a longer horizon, having 63.0% sensitivity 1 year before diagnosis and 44.2% 2 years before diagnosis, but with specificity remaining around 99%. CONCLUSIONS AND CLINICAL IMPORTANCE: The use of models based on machine learning can support veterinary decision making by improving early identification of CKD.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.