ArticleAnimals : an open access journal from MDPI2022
Artificial Intelligence for Lameness Detection in Horses-A Preliminary Study.
Article in Animals : an open access journal from MDPI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 18 citations in OpenAlex.
- Smartphone-Based Technologies in Equine Sports Medicine: Supporting Athlete Management-A Review.Sensors (Basel, Switzerland) · 2026Review
- A multimodal approach to equine thoracolumbar myofascial pain: integrating clinical examination, AI-based gait analysis, and the ridden horse pain ethogram.BMC veterinary research · 2026Article
- Priorities and Recommendations for Using Artificial Intelligence (AI) to Improve Equid Health and Welfare.Animals : an open access journal from MDPI · 2026Article
- A point-based laminitis risk scoring system and machine learning framework for prediction of laminitis in horses.Frontiers in veterinary science · 2026Article
- Automatic gait analysis in canines using computer vision.Frontiers in veterinary science · 2026Review
- An artificial intelligence-based diagnosis system for the identification of helminth parasitic infections in mithun and allied bovines.Scientific reports · 2025Article
- A Markerless Approach for Full-Body Biomechanics of Horses.Animals : an open access journal from MDPI · 2025Article
- Short-Term Impact of Dry Needling Treatment for Myofascial Pain on Equine Biomechanics Through Artificial Intelligence-Based Gait Analysis.Animals : an open access journal from MDPI · 2025Article
- Using deep learning models to decode emotional states in horses.Scientific reports · 2025Article
- Development of a Methodology for Low-Cost 3D Underwater Motion Capture: Application to the Biomechanics of Horse Swimming.Sensors (Basel, Switzerland) · 2023Article
- Validation of Alogo Move Pro: A GPS-Based Inertial Measurement Unit for the Objective Examination of Gait and Jumping in Horses.Sensors (Basel, Switzerland) · 2023Article
- Prediction of continuous and discrete kinetic parameters in horses from inertial measurement units data using recurrent artificial neural networks.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lameness in horses is a long-known issue influencing the welfare, as well as the use, of a horse. Nevertheless, the detection and classification of lameness mainly occurs on a subjective basis by the owner and the veterinarian. The aim of this study was the development of a lameness detection system based on pose estimation, which permits non-invasive and easily applicable gait analysis. The use of 58 reference points on easily detectable anatomical landmarks offers various possibilities for gait evaluation using a simple setup. For this study, three groups of horses were used: one training group, one analysis group of fore and hindlimb lame horses and a control group of sound horses. The first group was used to train the network; afterwards, horses with and without lameness were evaluated. The results show that forelimb lameness can be detected by visualising the trajectories of the reference points on the head and both forelimbs. In hindlimb lameness, the stifle showed promising results as a reference point, whereas the tuber coxae were deemed unsuitable as a reference point. The study presents a feasible application of pose estimation for lameness detection, but further development using a larger dataset is essential.
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