Assessing the feasibility of applying machine learning to diagnosing non-effusive feline infectious peritonitis

Author:

Dunbar Dawn,Babayan Simon A.,Krumrie Sarah,Haining Hayley,Hosie Margaret J.,Weir William

Abstract

AbstractFeline infectious peritonitis (FIP) is a severe feline coronavirus-associated syndrome in cats, which is invariably fatal without anti-viral treatment. In the majority of non-effusive FIP cases encountered in practice, confirmatory diagnostic testing is not undertaken and reliance is given to the interpretation of valuable, but essentially non-specific, clinical signs and laboratory markers. We hypothesised that it may be feasible to develop a machine learning (ML) approach which may be applied to the analysis of clinical data to aid in the diagnosis of disease. A dataset encompassing 1939 suspected FIP cases was scored for clinical suspicion of FIP on the basis of history, signalment, clinical signs and laboratory results, using published guidelines, comprising 683 FIP (35.2%), and 1256 non-FIP (64.8%) cases. This dataset was used to train, validate and evaluate two diagnostic machine learning ensemble models. These models, which analysed signalment and laboratory data alone, allowed the accurate discrimination of FIP and non-FIP cases in line with expert opinion. To evaluate whether these models may have value as a diagnostic tool, they were applied to a collection of 80 cases for which the FIP status had been confirmed (FIP: n = 58 (72.5%), non–FIP: n = 22 (27.5%)). Both ensemble models detected FIP with an accuracy of 97.5%, an area under the curve (AUC) of 0.969, sensitivity of 95.45% and specificity of 98.28%. This work demonstrates that, in principle, ML can be usefully applied to the diagnosis of non-effusive FIP. Further work is required before ML may be deployed in the laboratory as a diagnostic tool, such as training models on datasets of confirmed cases and accounting for inter-laboratory variation. Nevertheless, these results illustrate the potential benefit of applying ML to standardising and accelerating the interpretation of clinical pathology data, thereby improving the diagnostic utility of existing laboratory tests.

Publisher

Springer Science and Business Media LLC

Reference38 articles.

1. Pedersen, N. C. An update on feline infectious peritonitis: Diagnostics and therapeutics. Vet. J. 201, 133–141. https://doi.org/10.1016/j.tvjl.2014.04.016 (2014).

2. Weiss, R. C. & Scott, F. W. Pathogenesis of feline infetious peritonitis: Pathologic changes and immunofluorescence. Am. J. Vet. Res. 42, 2036–2048 (1981).

3. Cave, T. A., Golder, M. C., Simpson, J. & Addie, D. D. Risk factors for feline coronavirus seropositivity in cats relinquished to a UK rescue charity. J. Feline Med. Surg. 6, 53–58. https://doi.org/10.1016/j.jfms.2004.01.003 (2004).

4. Felten, S. et al. Correlation of feline coronavirus shedding in feces with coronavirus antibody titer. Pathogens https://doi.org/10.3390/pathogens9080598 (2020).

5. Taylor. S., T. S., Gunn-Moore. D., Barker. E, and Sorrell. S. An update on treatment of feline infectious peritonitis in the UK. https://www.vettimes.co.uk/article/an-update-on-treatment-of-feline-infectious-peritonitis-in-the-uk (2022).

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3