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 (Formula presented.)) 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.

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

Ferrini S.;Rollo C.;Bellino C.;Borriello G.;Cagnotti G.;Di Muro G.;Giacobini M.;D'Angelo A.
2023-01-01

Abstract

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 (Formula presented.)) 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
37
2
766
773
bovine neurology; central nervous system infections; clinical decision-making process; machine learning
Ferrini S.; Rollo C.; Bellino C.; Borriello G.; Cagnotti G.; Corona C.; Di Muro G.; Giacobini M.; Iulini B.; D'Angelo A.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1923151
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