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

Ann-Kristin Feuser1, Stefan Gesell-May2, Tobias Müller2

  • 1Equine Hospital in Parsdorf, 85599 Vaterstetten, Germany.

Animals : an Open Access Journal From MDPI
|October 27, 2022
PubMed
Summary
This summary is machine-generated.

This study developed a pose estimation system for non-invasive horse lameness detection. The system successfully identified forelimb lameness using head and forelimb reference points, offering a feasible approach for gait analysis.

Keywords:
artificial intelligencedeep learningequinelamenesspose estimation

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Area of Science:

  • Equine biomechanics
  • Veterinary diagnostics
  • Computer vision in animal science

Background:

  • Lameness in horses significantly impacts animal welfare and usability.
  • Current lameness detection relies heavily on subjective assessments by owners and veterinarians.
  • Objective, non-invasive gait analysis methods are needed for accurate diagnosis.

Purpose of the Study:

  • To develop a novel lameness detection system utilizing pose estimation technology.
  • To enable non-invasive and easily applicable gait analysis in horses.
  • To evaluate the efficacy of specific anatomical landmarks for lameness classification.

Main Methods:

  • A pose estimation system was developed using 58 reference points on anatomical landmarks.
  • A training group of horses was used to develop the detection network.
  • An analysis group comprising sound and lame horses (forelimb and hindlimb) was evaluated.

Main Results:

  • Forelimb lameness was effectively detected by analyzing trajectories of reference points on the head and forelimbs.
  • The stifle joint showed potential as a reference point for hindlimb lameness detection.
  • The tuber coxae proved unsuitable as a reference point for hindlimb lameness.

Conclusions:

  • Pose estimation offers a feasible and non-invasive method for equine lameness detection.
  • Further research with larger datasets is crucial to refine the system and improve accuracy.
  • This technology has the potential to revolutionize objective lameness diagnosis in horses.