An artificial neural network‐based model to predict chronic kidney disease in aged cats

Abstract

Background: Chronic kidney disease (CKD) frequently causes death in older cats; its early detection is challenging. Objectives: To build a sensitive and specific model for early prediction of CKD in cats using artificial neural network (ANN) techniques applied to routine health screening data. Animals: Data from 218 healthy cats ≥7 years of age screened at the Royal Veterinary College (RVC) were used for model building. Performance was tested using data from 3546 cats in the Banfield Pet Hospital records and an additional 60 RCV catsall initially without a CKD diagnosis. Methods: Artificial neural network (ANN) modeling used a multilayer feed-forward neural network incorporating a back-propagation algorithm. Clinical variables from single cat visits were selected using factorial discriminant analysis. Independent submodels were built for different prediction time frames. Two decision threshold strategies were investigated. Results: Input variables retained were plasma creatinine and blood urea concentrations, and urine specific gravity. For prediction of CKD within 12 months, the model had accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 88%, 87%, 70%, 53%, and 92%, respectively. An alternative decision threshold increased specificity and PPV to 98% and 87%, but decreased sensitivity and NPV to 42% and 79%, respectively. Conclusions and Clinical Importance: A model was generated that identified cats in the general population ≥7 years of age that are at risk of developing CKD within

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

“…The use of machine learning algorithms as a diagnostic tool in veterinary medicine is increasing in popularity. Recently, papers were published on hypoadrenocorticism [ 13 ], hyperadrenocorticism [ 15 ], early chronic kidney disease [ 31 ] and general chronic kidney disease [ 32 ]. As demonstrated in this work, it can be used as a tool to automatically uncover patterns in datasets.…”

Evaluation of a machine learning tool to screen for hypoadrenocorticism in dogs presenting to a teaching hospital

“…Machine learning methods have been applied to a wide range of clinical syndromes in people and veterinary medicine 9‐13 and can detect subtle patterns that allow for the early detection of disease. The gold standard diagnostic test for HA, serum cortisol measurement, has several shortcomings including the need for a clinician to recognize the need for the diagnostic test, prolonged turnaround time in some settings, increased cost to the client, and the need for specialized laboratory equipment to measure.…”

Artificial intelligence in veterinary diagnostic imaging: A literature review

“…Biourge et al 26 . used a NN to predict the onset of chronic kidney disease within 12 months in cats with 88% accuracy 26 . Kim et al.…”

“…Biourge et al 26 used a NN to predict the onset of chronic kidney disease within 12 months in cats with 88% accuracy. 26 Kim et al compared multiple NNs that classified the severity of canine ulcerative keratitis based on corneal photographs. 27 Artificial intelligence can also be used to perform the time-intensive task of converting written medical records to digital documents or searching digital documents for key words or phrases.…”

“…24 Clinically, AI has been applied to predict the need for surgery and survivability in colicking horses with 76% and 85% accuracy respectively by using history and clinical information 25 . Biourge et al 26 used a NN to predict the onset of chronic kidney disease within 12 months in cats with 88% accuracy. 26 Kim et al compared multiple NNs that classified the severity of canine ulcerative keratitis based on corneal photographs.…”