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Imada J, Arango-Sabogal JC, Bauman C, Roche S, Kelton D. Comparison of Machine Learning Tree-Based Algorithms to Predict Future Paratuberculosis ELISA Results Using Repeat Milk Tests. Animals (Basel) 2024; 14:1113. [PMID: 38612352 PMCID: PMC11011002 DOI: 10.3390/ani14071113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 03/28/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024] Open
Abstract
Machine learning algorithms have been applied to various animal husbandry and veterinary-related problems; however, its use in Johne's disease diagnosis and control is still in its infancy. The following proof-of-concept study explores the application of tree-based (decision trees and random forest) algorithms to analyze repeat milk testing data from 1197 Canadian dairy cows and the algorithms' ability to predict future Johne's test results. The random forest models using milk component testing results alongside past Johne's results demonstrated a good predictive performance for a future Johne's ELISA result with a dichotomous outcome (positive vs. negative). The final random forest model yielded a kappa of 0.626, a roc AUC of 0.915, a sensitivity of 72%, and a specificity of 98%. The positive predictive and negative predictive values were 0.81 and 0.97, respectively. The decision tree models provided an interpretable alternative to the random forest algorithms with a slight decrease in model sensitivity. The results of this research suggest a promising avenue for future targeted Johne's testing schemes. Further research is needed to validate these techniques in real-world settings and explore their incorporation in prevention and control programs.
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Affiliation(s)
- Jamie Imada
- Department of Population Medicine, University of Guelph, Guelph, ON N1G 2W1, Canada; (J.I.); (C.B.); (S.R.)
| | - Juan Carlos Arango-Sabogal
- Département de Pathologie et Microbiologie, Faculté de Médecine Vétérinaire, Université de Montréal, Saint-Hyacinthe, QC J2S 2M2, Canada;
| | - Cathy Bauman
- Department of Population Medicine, University of Guelph, Guelph, ON N1G 2W1, Canada; (J.I.); (C.B.); (S.R.)
| | - Steven Roche
- Department of Population Medicine, University of Guelph, Guelph, ON N1G 2W1, Canada; (J.I.); (C.B.); (S.R.)
- ACER Consulting, 100 Stone Rd West #101, Guelph, ON N1G 5L3, Canada
| | - David Kelton
- Department of Population Medicine, University of Guelph, Guelph, ON N1G 2W1, Canada; (J.I.); (C.B.); (S.R.)
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Farhoodimoghadam M, Reagan KL, Zwingenberger AL. Diagnosis and classification of portosystemic shunts: a machine learning retrospective case-control study. Front Vet Sci 2024; 11:1291318. [PMID: 38638645 PMCID: PMC11024426 DOI: 10.3389/fvets.2024.1291318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 03/21/2024] [Indexed: 04/20/2024] Open
Abstract
Diagnosis of portosystemic shunts (PSS) in dogs often requires multiple diagnostic tests, and available clinicopathologic tests have limitations in sensitivity and specificity. The objective of this study was to train and validate a machine learning model (MLM) that can accurately predict the presence of a PSS utilizing routinely collected demographic data and clinicopathologic features. Dogs diagnosed with PSS or control dogs tested for PSS but had the condition ruled out (non-PSS) were identified. Dogs were included if a complete blood count and serum chemistry panel were available from PSS diagnostic testing. Dogs with a PSS were subcategorized as having a single intrahepatic PSS, a single extrahepatic PSS, or multiple extrahepatic PSS. An extreme gradient boosting (XGboost) MLM was trained with data from 70% of the cases, and MLM performance was determined on the test set, comprising the remaining 30% of the case data. Two MLMs were created. The first was designed to predict the presence of any PSS (PSS MLM), and the second to predict the PSS subcategory (PSS SubCat MLM). The trained PSS MLM had a sensitivity of 94.3% (95% CI 90.1-96.8%) and specificity of 90.5% (95% CI 85.32-94.0%) for dogs in the test set. The area under the receiver operator characteristic curve (AUC) was 0.976 (95% CI; 0.964-0.989). The mean corpuscular hemoglobin, lymphocyte count, and serum globulin concentration were most important in prediction classification. The PSS SubCat MLM had an accuracy of 85.7% in determining the subtype of PSS of dogs in the test set, with variable sensitivity and specificity depending on PSS subtype. These MLMs have a high accuracy for diagnosing PSS; however, the prediction of PSS subclassification is less accurate. The MLMs can be used as a screening tool to increase or decrease the index of suspicion for PSS before confirmatory diagnostics such as advanced imaging are pursued.
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Affiliation(s)
- Makan Farhoodimoghadam
- Department of Computer Science, University of California, Davis, Davis, CA, United States
| | - Krystle L. Reagan
- Department of Veterinary Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
| | - Allison L. Zwingenberger
- Department of Surgical and Radiological Sciences, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
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Dunbar D, Babayan SA, Krumrie S, Haining H, Hosie MJ, Weir W. Assessing the feasibility of applying machine learning to diagnosing non-effusive feline infectious peritonitis. Sci Rep 2024; 14:2517. [PMID: 38291072 PMCID: PMC10827733 DOI: 10.1038/s41598-024-52577-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 01/20/2024] [Indexed: 02/01/2024] Open
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.
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Affiliation(s)
- Dawn Dunbar
- School of Biodiversity, One Health and Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G61 1QH, UK.
| | - Simon A Babayan
- School of Biodiversity, One Health and Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G61 1QH, UK
| | - Sarah Krumrie
- School of Biodiversity, One Health and Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G61 1QH, UK
| | - Hayley Haining
- School of Biodiversity, One Health and Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G61 1QH, UK
| | - Margaret J Hosie
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, G61 1QH, UK
| | - William Weir
- School of Biodiversity, One Health and Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G61 1QH, UK
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Guo W, Lv C, Guo M, Zhao Q, Yin X, Zhang L. Innovative applications of artificial intelligence in zoonotic disease management. SCIENCE IN ONE HEALTH 2023; 2:100045. [PMID: 39077042 PMCID: PMC11262289 DOI: 10.1016/j.soh.2023.100045] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 10/22/2023] [Indexed: 07/31/2024]
Abstract
Zoonotic diseases, transmitted between humans and animals, pose a substantial threat to global public health. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the fight against diseases. This comprehensive review discusses the innovative applications of AI in the management of zoonotic diseases, including disease prediction, early diagnosis, drug development, and future prospects. AI-driven predictive models leverage extensive datasets to predict disease outbreaks and transmission patterns, thereby facilitating proactive public health responses. Early diagnosis benefits from AI-powered diagnostic tools that expedite pathogen identification and containment. Furthermore, AI technologies have accelerated drug discovery by identifying potential drug targets and optimizing candidate drugs. This review addresses these advancements, while also examining the promising future of AI in zoonotic disease control. We emphasize the pivotal role of AI in revolutionizing our approach to managing zoonotic diseases and highlight its potential to safeguard the health of both humans and animals on a global scale.
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Affiliation(s)
- Wenqiang Guo
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Chenrui Lv
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Meng Guo
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450046, China
| | - Qiwei Zhao
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Xinyi Yin
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Li Zhang
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
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Sykes JE, Francey T, Schuller S, Stoddard RA, Cowgill LD, Moore GE. Updated ACVIM consensus statement on leptospirosis in dogs. J Vet Intern Med 2023; 37:1966-1982. [PMID: 37861061 PMCID: PMC10658540 DOI: 10.1111/jvim.16903] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 09/29/2023] [Indexed: 10/21/2023] Open
Abstract
Since publication of the last consensus statement on leptospirosis in dogs, there has been revision of leptospiral taxonomy and advancements in typing methods, widespread use of new diagnostic tests and vaccines, and improved understanding of the epidemiology and pathophysiology of the disease. Leptospirosis continues to be prevalent in dogs, including in small breed dogs from urban areas, puppies as young as 11 weeks of age, geriatric dogs, dogs in rural areas, and dogs that have been inadequately vaccinated for leptospirosis (including dogs vaccinated with 2-serovar Leptospira vaccines in some regions). In 2021, the American College of Veterinary Internal Medicine (ACVIM) Board of Regents voted to approve the topic for a revised Consensus Statement. After identification of core panelists, a multidisciplinary group of 6 experts from the fields of veterinary medicine, human medicine, and public health was assembled to vote on the recommendations using the Delphi method. A draft was presented at the 2023 ACVIM Forum, and a written draft posted on the ACVIM website for comment by the membership before submission to the editors of the Journal of Veterinary Internal Medicine. This revised document provides guidance for veterinary practitioners on disease in dogs as well as cats. The level of agreement among the 12 voting members (including core panelists) is provided in association with each recommendation. A denominator lower than 12 reflects abstention of ≥1 panelists either because they considered the recommendation to be outside their scope of expertise or because there was a perceived conflict of interest.
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Affiliation(s)
- Jane E. Sykes
- Department of Medicine and EpidemiologyUniversity of California, DavisDavisCalifornia95616USA
| | - Thierry Francey
- Department of Clinical Veterinary ScienceVetsuisse Faculty, University of BernBernSwitzerland
| | - Simone Schuller
- Department of Clinical Veterinary ScienceVetsuisse Faculty, University of BernBernSwitzerland
| | - Robyn A. Stoddard
- Bacterial Special Pathogens BranchCenters for Disease Control and PreventionAtlantaGeorgia30333USA
| | - Larry D Cowgill
- Department of Medicine and EpidemiologyUniversity of California, DavisDavisCalifornia95616USA
| | - George E. Moore
- Department of Veterinary AdministrationPurdue UniversityWest Lafayette, Indiana 47907USA
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Kim Y, Kim J, Kim S, Youn H, Choi J, Seo K. Machine learning-based risk prediction model for canine myxomatous mitral valve disease using electronic health record data. Front Vet Sci 2023; 10:1189157. [PMID: 37720471 PMCID: PMC10500836 DOI: 10.3389/fvets.2023.1189157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 08/15/2023] [Indexed: 09/19/2023] Open
Abstract
Introduction Myxomatous mitral valve disease (MMVD) is the most common cause of heart failure in dogs, and assessing the risk of heart failure in dogs with MMVD is often challenging. Machine learning applied to electronic health records (EHRs) is an effective tool for predicting prognosis in the medical field. This study aimed to develop machine learning-based heart failure risk prediction models for dogs with MMVD using a dataset of EHRs. Methods A total of 143 dogs with MMVD between May 2018 and May 2022. Complete medical records were reviewed for all patients. Demographic data, radiographic measurements, echocardiographic values, and laboratory results were obtained from the clinical database. Four machine-learning algorithms (random forest, K-nearest neighbors, naïve Bayes, support vector machine) were used to develop risk prediction models. Model performance was represented by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). The best-performing model was chosen for the feature-ranking process. Results The random forest model showed superior performance to the other models (AUC = 0.88), while the performance of the K-nearest neighbors model showed the lowest performance (AUC = 0.69). The top three models showed excellent performance (AUC ≥ 0.8). According to the random forest algorithm's feature ranking, echocardiographic and radiographic variables had the highest predictive values for heart failure, followed by packed cell volume (PCV) and respiratory rates. Among the electrolyte variables, chloride had the highest predictive value for heart failure. Discussion These machine-learning models will enable clinicians to support decision-making in estimating the prognosis of patients with MMVD.
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Affiliation(s)
- Yunji Kim
- Department of Veterinary Internal Medicine, College of Veterinary Medicine, Seoul, Republic of Korea
| | - Jaejin Kim
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Sehoon Kim
- Department of Veterinary Internal Medicine, College of Veterinary Medicine, Seoul, Republic of Korea
| | - Hwayoung Youn
- Department of Veterinary Internal Medicine, College of Veterinary Medicine, Seoul, Republic of Korea
| | - Jihye Choi
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Seoul National University, Seoul, Republic of Korea
| | - Kyoungwon Seo
- Department of Veterinary Internal Medicine, College of Veterinary Medicine, Seoul, Republic of Korea
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