A novel machine learning-based web application for field identification of infectious and inflammatory disorders of the central nervous system in cattle.

Autor: Ferrini S; Department of Veterinary Sciences, University of Turin, Turin, Italy., Rollo C; Department of Medical Sciences, University of Turin, Turin, Italy., Bellino C; Department of Veterinary Sciences, University of Turin, Turin, Italy., Borriello G; Department of Veterinary Sciences, University of Turin, Turin, Italy., Cagnotti G; Department of Veterinary Sciences, University of Turin, Turin, Italy., Corona C; Istituto Zooprofilattico del Piemonte Liguria e Valle d'Aosta, Turin, Italy., Di Muro G; Department of Veterinary Sciences, University of Turin, Turin, Italy., Giacobini M; Department of Veterinary Sciences, University of Turin, Turin, Italy., Iulini B; Istituto Zooprofilattico del Piemonte Liguria e Valle d'Aosta, Turin, Italy., D'Angelo A; Department of Veterinary Sciences, University of Turin, Turin, Italy.
Jazyk: angličtina
Zdroj: Journal of veterinary internal medicine [J Vet Intern Med] 2023 Mar; Vol. 37 (2), pp. 766-773. Date of Electronic Publication: 2023 Mar 10.
DOI: 10.1111/jvim.16664
Abstrakt: Background: Central nervous system (CNS) infections in cattle are a major cause of economic loss and mortality. Machine learning (ML) techniques are gaining widespread application in solving predictive tasks in both human and veterinary medicine.
Objectives: Our primary aim was to develop and compare ML models that could predict the likelihood of a CNS disorder of infectious or inflammatory origin in neurologically-impaired cattle. Our secondary aim was to create a user-friendly web application based on the ML model for the diagnosis of infection and inflammation of the CNS.
Animals: Ninety-eight cattle with CNS infection and 86 with CNS disorders of other origin.
Methods: Retrospective observational study. Six different ML methods (logistic regression [LR]; support vector machine [SVM]; random forest [RF]; multilayer perceptron [MLP]; K-nearest neighbors [KNN]; gradient boosting [GB]) were compared for their ability to predict whether an infectious or inflammatory disease was present based on demographics, neurological examination findings, and cerebrospinal fluid (CSF) analysis.
Results: All 6 methods had high prediction accuracy (≥80%). The accuracy of the LR model was significantly higher (0.843 ± 0.005; receiver operating characteristic [ROC] curve 0.907 ± 0.005 ) than the other models and was selected for implementation in a web application.
Conclusion and Clinical Importance: Our findings support the use of ML algorithms as promising tools for veterinarians to improve diagnosis. The open-access web application may aid clinicians in achieving correct diagnosis of infectious and inflammatory neurological disorders in livestock, with the added benefit of promoting appropriate use of antimicrobials.
(© 2023 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals LLC on behalf of American College of Veterinary Internal Medicine.)
Databáze: MEDLINE
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