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Animals : an open access journal from MDPI2022; 12(24); 3498; doi: 10.3390/ani12243498

Investigation of Thresholds for Asymmetry Indices to Represent the Visual Assessment of Single Limb Lameness by Expert Veterinarians on Horses Trotting in a Straight Line.

Abstract: Defining whether a gait asymmetry should be considered as lameness is challenging. Gait analysis systems now provide relatively accurate objective data, but their interpretation remains complex. Thresholds for discriminating between horses that are visually assessed as being lame or sound, as well as thresholds for locating the lame limb with precise sensitivity and specificity are essential for accurate interpretation of asymmetry measures. The goal of this study was to establish the thresholds of asymmetry indices having the best sensitivity and specificity to represent the visual single-limb lameness assessment made by expert veterinarians as part of their routine practice. Horses included in this study were evaluated for locomotor disorders at a clinic and equipped with the EQUISYM system using inertial measurement unit (IMU) sensors. Visual evaluation by expert clinicians allocated horses into five groups: 49 sound, 62 left forelimb lame, 67 right forelimb lame, 23 left hindlimb lame, and 23 right hindlimb lame horses. 1/10 grade lame horses were excluded. Sensors placed on the head (_H), the withers (_W), and the pelvis (_P) provided vertical displacement. Relative difference of minimal (AI-min) and maximal (AI-max) altitudes, and of upward (AI-up) and downward (AI-down) amplitudes between right and left stance phases were calculated. Receiver operating characteristic (ROC) curves discriminating the sound horses from each lame limb group revealed the threshold of asymmetry indice associated with the best sensitivity and specificity. AI-up_W had the best ability to discriminate forelimb lame horses from sound horses with thresholds (left: -7%; right: +10%) whose sensitivity was greater than 84% and specificity greater than 88%. AI-up_P and AI-max_P discriminated hindlimb lame horses from sound horses with thresholds (left: -7%; right: +18% and left: -10%; right: +6%) whose sensitivity was greater than 78%, and specificity greater than 82%. Identified thresholds will enable the interpretation of quantitative data from lameness quantification systems. This study is mainly limited by the number of included horses and deserves further investigation with additional data, and similar studies on circles are warranted.
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Publication Date: 2022-12-11 PubMed ID: 36552418PubMed Central: PMC9774792DOI: 10.3390/ani12243498Google 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.

The research article focuses on developing reliable thresholds for detecting lameness in horses through analyzing gait asymmetry using technology like the EQUISYM system. The study aimed to enhance the accuracy of detecting and locating lameness in horses by expert veterinarians as part of their routine procedures.

Methodology

  • The researchers involved a cohort of horses, which were evaluated for locomotion related disorders at a clinic. These horses were equipped with the EQUISYM system that uses inertial measurement unit (IMU) sensors.
  • Following visual evaluation by expert clinicians, the horses were allocated into five different groups: sound horses (49), left forelimb lame horses (62), right forelimb lame horses (67), left hindlimb lame horses (23) and right hindlimb lame horses (23).
  • To measure vertical displacement, sensors were placed on the head (_H), the withers (_W), and the pelvis (_P) of the horses.
  • Calculations were made for the relative difference of minimal (AI-min) and maximal (AI-max) altitudes, and of upward (AI-up) and downward (AI-down) amplitudes between right and left stance phases.

Analysis and Results

  • The researchers utilized Receiver Operating Characteristic (ROC) curves to differentiate the sound horses from each lame limb group. This method helped to reveal the threshold of asymmetry index associated with the best sensitivity and specificity.
  • It was found that AI-up_W manifested the best ability to discriminate forelimb lame horses from sound horses with thresholds (left: -7%; right: +10%) whose sensitivity was greater than 84% and specificity greater than 88%.
  • For distinguishing hindlimb lame horses from sound horses, AI-up_P and AI-max_P were effective with thresholds (left: -7%; right: +18% and left: -10%; right: +6%). These thresholds demonstrated sensitivity greater than 78%, and specificity greater than 82%.

Implications and Limitations

  • The thresholds identified will aid in interpreting quantitative data from lameness quantification systems, thereby assisting in accurate and efficient detection of lameness in horses.
  • The study, however, is restricted by the number of horses included. Thus, it calls for further investigation with an increased volume of data.
  • They also recommended performing similar studies on circles for more in-depth exploration.

Cite This Article

APA
Macaire C, Hanne-Poujade S, De Azevedo E, Denoix JM, Coudry V, Jacquet S, Bertoni L, Tallaj A, Audigié F, Hatrisse C, Hébert C, Martin P, Marin F, Chateau H. (2022). Investigation of Thresholds for Asymmetry Indices to Represent the Visual Assessment of Single Limb Lameness by Expert Veterinarians on Horses Trotting in a Straight Line. Animals (Basel), 12(24), 3498. https://doi.org/10.3390/ani12243498

Publication

Animals : an open access journal from MDPI
ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 12
Issue: 24
PII: 3498

Researcher Affiliations

Macaire, Claire
  • LIM France, Labcom LIM-ENVA, 24300 Nontron, France.
  • Ecole Nationale Vétérinaire d'Alfort, USC INRAE-ENVA 957 BPLC, CIRALE, 94700 Maisons-Alfort, France.
  • Laboratoire de BioMécanique et BioIngénierie (UMR CNRS 7338), Centre of Excellence for Human and Animal Movement Biomechanics (CoEMoB), Université de Technologie de Compiègne (UTC), Alliance Sorbonne Université, 60200 Compiègne, France.
Hanne-Poujade, Sandrine
  • LIM France, Labcom LIM-ENVA, 24300 Nontron, France.
De Azevedo, Emeline
  • Ecole Nationale Vétérinaire d'Alfort, USC INRAE-ENVA 957 BPLC, CIRALE, 94700 Maisons-Alfort, France.
Denoix, Jean-Marie
  • Ecole Nationale Vétérinaire d'Alfort, USC INRAE-ENVA 957 BPLC, CIRALE, 94700 Maisons-Alfort, France.
Coudry, Virginie
  • Ecole Nationale Vétérinaire d'Alfort, USC INRAE-ENVA 957 BPLC, CIRALE, 94700 Maisons-Alfort, France.
Jacquet, Sandrine
  • Ecole Nationale Vétérinaire d'Alfort, USC INRAE-ENVA 957 BPLC, CIRALE, 94700 Maisons-Alfort, France.
Bertoni, Lélia
  • Ecole Nationale Vétérinaire d'Alfort, USC INRAE-ENVA 957 BPLC, CIRALE, 94700 Maisons-Alfort, France.
Tallaj, Amélie
  • Ecole Nationale Vétérinaire d'Alfort, USC INRAE-ENVA 957 BPLC, CIRALE, 94700 Maisons-Alfort, France.
Audigié, Fabrice
  • Ecole Nationale Vétérinaire d'Alfort, USC INRAE-ENVA 957 BPLC, CIRALE, 94700 Maisons-Alfort, France.
Hatrisse, Chloé
  • Ecole Nationale Vétérinaire d'Alfort, USC INRAE-ENVA 957 BPLC, CIRALE, 94700 Maisons-Alfort, France.
Hébert, Camille
  • LIM France, Labcom LIM-ENVA, 24300 Nontron, France.
Martin, Pauline
  • LIM France, Labcom LIM-ENVA, 24300 Nontron, France.
Marin, Frédéric
  • Laboratoire de BioMécanique et BioIngénierie (UMR CNRS 7338), Centre of Excellence for Human and Animal Movement Biomechanics (CoEMoB), Université de Technologie de Compiègne (UTC), Alliance Sorbonne Université, 60200 Compiègne, France.
Chateau, Henry
  • Ecole Nationale Vétérinaire d'Alfort, USC INRAE-ENVA 957 BPLC, CIRALE, 94700 Maisons-Alfort, France.

Grant Funding

  • 20E01636 / European Regional Development Fund
  • ANR 16-LCV2-0002-01 / Agence Nationale de la Recherche

Conflict of Interest Statement

The project was supported by the Agence Nationale de la Recherche (ANR) which sponsors a collaborative project between a company (LIM France) and the Ecole Nationale Vétérinaire d’Alfort (ENVA).

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Citations

This article has been cited 10 times.
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