Assessing the feasibility of applying machine learning to diagnosing non-effusive feline infectious peritonitis
Abstract Feline 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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-52577-4 |
| _version_ | 1797274673030889472 |
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| author | Dawn Dunbar Simon A. Babayan Sarah Krumrie Hayley Haining Margaret J. Hosie William Weir |
| author_facet | Dawn Dunbar Simon A. Babayan Sarah Krumrie Hayley Haining Margaret J. Hosie William Weir |
| author_sort | Dawn Dunbar |
| collection | DOAJ |
| description | Abstract Feline 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. |
| first_indexed | 2024-03-07T15:01:39Z |
| format | Article |
| id | doaj.art-b0a6a964cab441f09202f4a3793ac3b4 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:01:39Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b0a6a964cab441f09202f4a3793ac3b42024-03-05T19:07:30ZengNature PortfolioScientific Reports2045-23222024-01-0114111510.1038/s41598-024-52577-4Assessing the feasibility of applying machine learning to diagnosing non-effusive feline infectious peritonitisDawn Dunbar0Simon A. Babayan1Sarah Krumrie2Hayley Haining3Margaret J. Hosie4William Weir5School of Biodiversity, One Health and Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of GlasgowSchool of Biodiversity, One Health and Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of GlasgowSchool of Biodiversity, One Health and Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of GlasgowSchool of Biodiversity, One Health and Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of GlasgowMRC-University of Glasgow Centre for Virus Research, University of GlasgowSchool of Biodiversity, One Health and Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of GlasgowAbstract Feline 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.https://doi.org/10.1038/s41598-024-52577-4 |
| spellingShingle | Dawn Dunbar Simon A. Babayan Sarah Krumrie Hayley Haining Margaret J. Hosie William Weir Assessing the feasibility of applying machine learning to diagnosing non-effusive feline infectious peritonitis Scientific Reports |
| title | Assessing the feasibility of applying machine learning to diagnosing non-effusive feline infectious peritonitis |
| title_full | Assessing the feasibility of applying machine learning to diagnosing non-effusive feline infectious peritonitis |
| title_fullStr | Assessing the feasibility of applying machine learning to diagnosing non-effusive feline infectious peritonitis |
| title_full_unstemmed | Assessing the feasibility of applying machine learning to diagnosing non-effusive feline infectious peritonitis |
| title_short | Assessing the feasibility of applying machine learning to diagnosing non-effusive feline infectious peritonitis |
| title_sort | assessing the feasibility of applying machine learning to diagnosing non effusive feline infectious peritonitis |
| url | https://doi.org/10.1038/s41598-024-52577-4 |
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