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Animals : an open access journal from MDPI2022; 12(20); 2804; doi: 10.3390/ani12202804

Artificial Intelligence for Lameness Detection in Horses-A Preliminary Study.

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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Publication Date: 2022-10-17 PubMed ID: 36290189PubMed Central: PMC9597839DOI: 10.3390/ani12202804Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research involved the development of a system for detecting lameness in horses using pose estimation. The aim was to devise a non-invasive method for analyzing a horse’s gait, using a network of 58 reference points on visible anatomical landmarks. This was trialed on three groups of horses (one for training purposes, another for analyzing both forelimb and hindlimb lame horses, and a control group of sound horses). Results indicate that this method shows promise but needs further development using larger datasets.

Objective of the Research

  • The study aimed at creating an objective, non-invasive system to detect lameness in horses. The primary technique used is pose estimation, relying on identifiable anatomical landmarks on the horse’s body.

Methodology

  • The research involved 58 reference points, chosen based on their easy visibility and relevance to the horse’s gait.
  • Three groups of horses were involved: a training group, an analysis group comprising forelimb and hindlimb lame horses, and a control group of healthy horses.
  • The training group was used to train the AI system.
  • Upon successful training, the system was tested on the analysis group (lame horses) and the control group (healthy horses).

Results and Findings

  • Forelimb lameness detection was achieved by visualising the trajectories of reference points on the horse’s head and both forelimbs.
  • Hindlimb lameness detection was more challenging. The stifle (joint above the horse’s hind leg) proved useful as a reference point.
  • The tuber coxae (hip bone region of the horse) was found to be unsuitable as a reference point.

Conclusions

  • This study provided a potential application of pose estimation in diagnosing lameness in horses.
  • Despite the promising results, the research acknowledges the need for further development and expansion of the study using larger datasets to establish the system’s accuracy and reliability.

Cite This Article

APA
Feuser AK, Gesell-May S, Müller T, May A. (2022). Artificial Intelligence for Lameness Detection in Horses-A Preliminary Study. Animals (Basel), 12(20), 2804. https://doi.org/10.3390/ani12202804

Publication

Animals : an open access journal from MDPI
ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 12
Issue: 20
PII: 2804

Researcher Affiliations

Feuser, Ann-Kristin
  • Equine Hospital in Parsdorf, 85599 Vaterstetten, Germany.
Gesell-May, Stefan
  • Anirec GmbH, Artificial Intelligence Solutions in Veterinary Medicine, 80539 Munich, Germany.
Müller, Tobias
  • Anirec GmbH, Artificial Intelligence Solutions in Veterinary Medicine, 80539 Munich, Germany.
May, Anna
  • Equine Hospital, Ludwig Maximilians University, 85764 Oberschleissheim, Germany.

Conflict of Interest Statement

The authors declare no conflict of interest.

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