ArticleVeterinary medicine and science2025
Evaluating the Reliability and Accuracy of Senior Veterinary Students in Detecting and Scoring Lameness in Dairy Cows.
Article in Veterinary medicine and science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Hooves, Sensors, and Signals: Precision Approaches to Automated Lameness Detection in Dairy Cattle.Animals : an open access journal from MDPI · 2026Review
- Lameness detection in dairy cows from overhead view: high-precision keypoint localization and multi-feature fusion classification.Frontiers in veterinary science · 2025Article
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Authors and funding
2 authors.
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Abstract
backgroundLameness detection is essential for effective dairy cattle management, with accurate diagnosis improving animal welfare and reducing economic losses. Senior veterinary students must acquire these skills before graduation to ensure competent diagnosis in the field.
objectivesThis study aimed to evaluate the reliability and accuracy of senior veterinary students in detecting and scoring lameness in dairy cows.
methodsThe study included 201 senior veterinary students who scored lameness in cows using video recordings and a 5-point scoring system. Students' lameness scores were compared to those assigned by an experienced observer using a confusion matrix, with sensitivity, specificity, and accuracy calculated. Intra-rater reliability was assessed using intraclass correlation coefficients, while inter-rater reliability was evaluated using Krippendorff's alpha. Binary logistic regression was performed to assess the impact of lameness severity on detection accuracy.
resultsStudents demonstrated high accuracy for severe lameness (93.67%) and healthy cases (85.93%), with sensitivities of 75.84% and 74.46%, respectively. However, sensitivity for mild to moderate lameness was lowest. Specificity ranged from 81.87% for mild cases to 98.12% for severe cases. Inter- and intra-rater reliability showed various agreement coefficients across lameness categories. Logistic regression indicated decreased accuracy with increasing lameness severity.
conclusionGaps in detecting intermediate lameness highlight the need for enhanced training methods in veterinary education. Integrating advanced tools can improve diagnostic accuracy and support better lameness detection in practice.
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