Applying Supervised Machine Learning to Effusion Analysis for the Diagnosis of Feline Infectious Peritonitis

Abstract

Feline infectious peritonitis (FIP) is a major disease of cats which, unless promptly diagnosed and treated, is invariably fatal. Although it has long been recognised that the condition is the result of an aberrant immune response to infection with feline coronavirus, there remain significant gaps in our understanding of its pathogenesis. Consequently, diagnosis is complex and relies on the combined interpretation of numerous clinical signs and laboratory biomarkers, many of which are non-specific. In the case of effusive FIP, a commonly encountered acute form of the disease where body cavity effusions develop; the interpretation of fluid analysis results is key to diagnosing the condition. We hypothesised that machine learning could be applied to fluid analysis test data in order to help diagnose effusive FIP. Thus, historical test records from a veterinary laboratory dataset of 718 suspected cases of effusive disease were identified, representing 336 cases of FIP and 382 cases that were determined not to be FIP. This dataset was used to train an ensemble model to predict disease status based on clinical observations and laboratory features. Our model predicts the correct disease state with an accuracy of 96.51%, an area under the receiver operator curve of 96.48%, a sensitivity of 98.85% and a specificity of 94.12%. This study demonstrates that machine learning can be successfully applied to the interpretation of fluid analysis results to accurately detect cases of effusive FIP. Thus, this method has the potential to be utilised in a veterinary diagnostic laboratory setting to standardise and improve service provision.