Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice

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.

Metabolomic Abnormalities in Serum from Untreated and Treated Dogs with Hyper- and Hypoadrenocorticism

“…While a prior study predicted canine hyperadrenocorticism based on demographic data, clinical signs, and liver enzyme activities [45] and another predicted canine hypoadrenocorticism based on CBC and serum chemistry screening results [46], this is the first study to apply machine learning approaches based solely on metabolomics data. The applied machine learning tools performed well, predicting the correct groups when applied to the two untreated endocrinopathies and the control group (Tables 2 and S2).…”

Non-destructive diagnostic testing of cardiac myxoma by serum confocal Raman microspectroscopy combined with multivariate analysis

“…SVM, developed by Vapnik and Burges, is considered superior to traditional linear methods due to its ability to represent nonlinear features in data. 23,24 As a result, SVM is attracting increasing interest for the classification of spectral data. 25–28 Up to now, SVM detection of CM has not been investigated.…”

External validation of a United Kingdom primary‐care Cushing's prediction tool in a population of referred Dutch dogs

“…However, none of these tests are perfect, as they can be time‐consuming, costly, and both false‐positive and false‐negative results are common 6‐12 . Recently, a prediction tool was developed and internally validated to aid the diagnosis of spontaneous Cushing's syndrome in dogs 13,14 . This model demonstrated a good predictive performance in dogs attending UK primary‐care practices, using neuter status, age, breed, polydipsia, vomiting, potbelly/hepatomegaly, alopecia, pruritus, urine specific gravity (USG), and serum ALP as predictor variables.…”