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Asymmetry Thresholds Reflecting the Visual Assessment of Forelimb Lameness on Circles on a Hard Surface.

Claire MacaireSandrine Hanne-PoujadeEmeline De AzevedoJean-Marie DenoixVirginie CoudrySandrine JacquetLélia BertoniAmélie TallajFabrice AudigiéChloé HatrisseCamille HébertPauline MartinFrederic MarinHenry Chateau
Published in: Animals : an open access journal from MDPI (2023)
The assessment of lameness in horses can be aided by objective gait analysis tools. Despite their key role of evaluating a horse at trot on a circle, asymmetry thresholds have not been determined for differentiating between sound and lame gait during this exercise. These thresholds are essential to distinguish physiological asymmetry linked to the circle from pathological asymmetry linked to lameness. This study aims to determine the Asymmetry Indices (AIs) with the highest power to discriminate between a group of sound horses and a group of horses with consistent unilateral lameness across both circle directions, as categorized by visual lameness assessment conducted by specialist veterinarians. Then, thresholds were defined for the best performing AIs, based on the optimal sensitivity and specificity. AIs were calculated as the relative comparison between left and right minima, maxima, time between maxima and upward amplitudes of the vertical displacement of the head and the withers. Except the AI of maxima difference, the head AI showed the highest sensitivity (≥69%) and the highest specificity (≥81%) for inside forelimb lameness detection and the withers AI showed the highest sensitivity (≥72%) and the highest specificity (≥77%) for outside forelimb lameness detection on circles.
Keyphrases
  • artificial intelligence
  • palliative care
  • label free
  • machine learning
  • high intensity
  • computed tomography
  • magnetic resonance imaging
  • real time pcr
  • contrast enhanced