ArticleEquine veterinary journal2026
Agreement between subjective gait assessment and markerless video gait-analysis in endurance horses.
Article in Equine veterinary journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Smartphone-Based Technologies in Equine Sports Medicine: Supporting Athlete Management-A Review.Sensors (Basel, Switzerland) · 2026Review
- Agreement between subjective gait assessment and markerless video gait-analysis in endurance horses.Equine veterinary journal · 2026Article
- Comparison of Gait Characteristics for Horses Without Shoes, with Steel Shoes, and with Aluminum Shoes.Animals : an open access journal from MDPI · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundSubjective evaluation of gait by official endurance veterinarians (OEVs) is used to determine 'fitness-to-compete' in horses participating in endurance competitions. Objective gait analysis systems could aid in quick and verifiable judgements.
objectivesTo assess the agreement between objective analysis of head and pelvis vertical movement asymmetry performed with a markerless artificial intelligence motion tracking system (AI-MTS) and subjective lameness assessment performed by an accredited FEI OEV to judge horse gaits. STUDY
designCross-sectional.
methodsDuring three endurance competitions, 110 horses were enrolled. The OEV performed 188 gait examinations, which were simultaneously recorded with a smartphone. The vertical motion asymmetry of the head and pelvis was later analysed from the videos through the AI-MTS application. The gaits were scored as 'no asymmetry', 'mild asymmetry' or 'severe asymmetry'. The agreement was evaluated using Fleiss' multi-rater kappa statistic (κ).
resultsThe overall agreement between the two methods was fair (k = 0.26, p < 0.001). Within the three gait asymmetry categories, substantial agreement was obtained for the 'severe' (k = 0.75, p < 0.001) category, fair agreement was detected for the 'no asymmetry' category (k = 0.25, p < 0.001), and no agreement was identified for the 'mild' category (k = 0.13, p = 0.08). MAIN LIMITATIONS: Comparison between AI-MTS and a single OEV; absence of a tripod during video recording; and video recording from a different point of view than the OEVs.
conclusionsMild asymmetry was the most challenging gait category to identify. Substantial agreement between the subjective lameness evaluation by OEV and AI-MTS assessment was observed for the 'severe' category. AI-MTS may be a helpful tool to assist OEVs in decision-making during endurance competitions.
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