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Objective movement asymmetry in horses is comparable between markerless technology and sensor-based systems.

Anne S Kallerud1, Patrick Marques-Smith1, Helle K Bendiksen2

  • 1Department of Companion Animal Clinical Sciences, Norwegian University of Life Sciences, Aas, Norway.

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PubMed
Summary

Markerless artificial intelligence (AI) and inertial measurement unit (IMU) systems showed comparable lameness detection in horses. Agreement was strongest between two IMU systems, but AI analyzed fewer strides, potentially limiting its effectiveness.

Keywords:
artificial intelligencecomputer visionhorsehorse lameness

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Area of Science:

  • Equine biomechanics and locomotion analysis.
  • Application of artificial intelligence and sensor technology in animal health.

Background:

  • A novel markerless AI system for lameness detection is available but lacks extensive comparison with established inertial measurement unit (IMU) systems.
  • Field condition assessments are crucial for evaluating the practical utility of lameness detection technologies in horses.

Purpose of the Study:

  • To compare the classification of asymmetric limbs under field conditions using a markerless AI system (SleipAI; SL) and two IMU systems (Equinosis Q Lameness Locator; LL, EquiMoves; EM).
  • To compare normalized asymmetry data derived from these objective systems and subjective evaluation (SE).

Main Methods:

  • A descriptive clinical study involving 52 client-owned horses in regular training.
  • Data collection during straight-line trot, with limbs categorized as symmetric or asymmetric.
  • Statistical analysis included Wilcoxon's test for stride comparison, Light's Kappa for inter-rater reliability, and Bland-Altman analysis for normalized asymmetry data.

Main Results:

  • Data from 41 horses were analyzed, with most exhibiting mild asymmetry.
  • EquiMoves (EM) analyzed significantly more strides (forelimbs and hindlimbs) compared to other systems.
  • Moderate inter-rater agreement for asymmetry classification was observed between systems (k=0.59 forelimbs, 0.44 hindlimbs), decreasing with subjective evaluation inclusion. Strongest agreement for normalized asymmetry data was between the two IMU systems (LL and EM).

Conclusions:

  • Objective lameness detection systems demonstrated comparability in classifying asymmetric limbs under field conditions, with discrepancies often related to defined asymmetry thresholds.
  • The SleipAI (SL) system analyzed significantly fewer hindlimb strides than LL and EM, indicating a potential limitation.
  • The strongest agreement in normalized asymmetry data was found between the Equinosis Q Lameness Locator (LL) and EquiMoves (EM) systems.