[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124475-en":3,"doc-seo-124475-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124475,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Applying Supervised Machine Learning to Effusion Analysis for the Diagnosis of Feline Infectious Peritonitis","Feline infectious peritonitis (FIP) is a major, usually fatal cat disease when not diagnosed and treated promptly. Although FIP stems from an aberrant immune response to feline coronavirus, its pathogenesis remains incompletely understood, making diagnosis difficult and dependent on complex interpretation of clinical signs and non-specific laboratory biomarkers. For effusive FIP, where body cavity effusions develop, machine learning is applied to fluid analysis test data. Using 718 suspected effusive cases (336 FIP, 382 non-FIP), an ensemble model predicts disease status with 96.51% accuracy (AUC 96.48%), showing 98.85% sensitivity and 94.12% specificity. This approach supports standardized, improved diagnostic services in veterinary laboratories.","Article  \nApplying Supervised Machine Learning to Effusion Analysis for the Diagnosis of Feline Infectious Peritonitis  \nDawn E. Dunbar 1, *, Simon A. Babayan 1, Sarah Krumrie 2, Sharmila Rennie 1, Elspeth M. Waugh 1, Margaret J. Hosie 3 and William Weir 1  \nAcademic Editor: Bernard Chiu  \nReceived: 2 November 2025  \nRevised: 12 January 2026  \nAccepted: 20 January 2026  \nPublished: 23 January 2026  \nCopyright: © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.  \n1 School of Biodiversity, One Health and Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK; [simon.babayan@glasgow.ac.uk](simon.babayan@glasgow.ac.uk) (S.A.B.);  \n[sharmila.rennie@glasgow.ac.uk](sharmila.rennie@glasgow.ac.uk) (S.R.); [elspeth.waugh@glasgow.ac.uk](elspeth.waugh@glasgow.ac.uk) (E.M.W.);  \n[willie.weir@glasgow.ac.uk](willie.weir@glasgow.ac.uk) (W.W.)  \n2 Dunbia, The Summit, Pynes Hill, Exeter EX2 5WS, UK  \n3 MRC-University of Glasgow Centre for Virus Research, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK; [margaret.hosie@glasgow.ac.uk](margaret.hosie@glasgow.ac.uk)  \n* Correspondence: [dawn.dunbar@glasgow.ac.uk](dawn.dunbar@glasgow.ac.uk)  \nAbstract  \nFeline 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.  \nKeywords: feline infectious peritonitis; feline coronavirus; machine learning; diagnostic; modelling; predictive  \n1. Introduction  \nFeline infectious peritonitis is one of the most difficult to diagnose viral diseases of cats, and it was, until recently, one of the most common infectious causes of mortality in felids [1,2] . Feline coronavirus (FCoV) is the infectious agent responsible for infection and subsequent disease. This virus is ubiquitous in the feline population and can infect both domestic and wild felids, causing FIP [3], although typically the virus only causes a sub-clinical or mild-moderate enteritis [4–6] . A number of virally encoded genes have been  \nimplicated in determining viral cell tropism. In enteric infection the virus targets enterocytes whereas FIP is believed to develop following a switch in viral tropism, resulting in the virus replicating to high titres in the monocyte/macrophage lineages of leukocytes [1,7] ","cbCaijThqTKNUFON","https://ap.wps.com/l/cbCaijThqTKNUFON","pdf",25958379,1,26,"English","en",105,"# Introduction\n## Background on FIP and feline coronavirus\n## Diagnostic challenges and role of effusions\n# Methods\n## Dataset of suspected effusive disease cases\n## Ensemble model training and input features\n# Results\n## Predictive performance metrics\n# Discussion\n## Clinical value for veterinary diagnostic laboratories\n# Conclusion","[{\"question\":\"Why is diagnosing effusive feline infectious peritonitis difficult?\",\"answer\":\"Diagnosis is complex because it relies on combined interpretation of many clinical signs and laboratory biomarkers, many of which are non-specific. Effusive FIP also depends on interpreting fluid analysis results where effusions are present.\"},{\"question\":\"How was the machine learning model trained in the study?\",\"answer\":\"Historical veterinary laboratory records from a dataset of 718 suspected effusive cases were used, including 336 FIP cases and 382 non-FIP cases. The ensemble model was trained to predict disease status from clinical observations and laboratory features.\"},{\"question\":\"What level of accuracy did the model achieve?\",\"answer\":\"The model predicted the correct disease state with 96.51% accuracy and an AUC of 96.48%. Sensitivity was 98.85% and specificity was 94.12%.\"}]","Applying Supervised Machine Learning to Effusion Analysis for the Diagnosis of Feline Infectious Peritonitis | PDF",1785822655,66,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"applying-supervised-machine-learning-to-effusion-analysis-for-the-diagnosis-of-feline-infectious-peritonitis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/applying-supervised-machine-learning-to-effusion-analysis-for-the-diagnosis-of-feline-infectious-peritonitis/124475/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is diagnosing effusive feline infectious peritonitis difficult?","Question",{"text":75,"@type":76},"Diagnosis is complex because it relies on combined interpretation of many clinical signs and laboratory biomarkers, many of which are non-specific. Effusive FIP also depends on interpreting fluid analysis results where effusions are present.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the machine learning model trained in the study?",{"text":80,"@type":76},"Historical veterinary laboratory records from a dataset of 718 suspected effusive cases were used, including 336 FIP cases and 382 non-FIP cases. The ensemble model was trained to predict disease status from clinical observations and laboratory features.",{"name":82,"@type":73,"acceptedAnswer":83},"What level of accuracy did the model achieve?",{"text":84,"@type":76},"The model predicted the correct disease state with 96.51% accuracy and an AUC of 96.48%. 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