Abstract

Background: In horses, osteoarthritis (OA) mostly affects metacarpophalangeal and metatarsophalangeal (fetlock) joints. The current modalities used for diagnosis of equine limb disorders lack ability to detect early OA. Here, we propose a new alternative approach to assess experimental cartilage damage in fetlock joint using Acoustic Emissions (AE).

Objectives: To evaluate the potential of AE technique in diagnosing OA and see how AE signals changes with increasing severity of OA.

Study design: An in vitro experimental study.

Methods: A total of 16 distal limbs (8 forelimbs and 8 hindlimbs) from six Finn horses were collected from an abattoir and fitted in a custom‐made frame allowing fetlock joint bending. Eight fetlock joints were opened, and cartilage surface was progressively damaged mechanically three times using sandpaper to mimic mild, moderate and severe OA. The remaining eight fetlock joints were opened and closed without any mechanical procedure, serving as controls. Before cartilage alteration, synovial fluid was aspirated, mixed with phosphate‐buffered saline solution, and then reinjected before suturing for constant joint lubrication. For each simulated condition of OA severity, a force was applied to the frame and then released to mimic joint flexion and extension. AE signals were acquired using air microphones.

Results: A strong association was found between the joint condition and the power of AE signals analysed in 1.5–6 kHz range. The signal from both forelimb and hindlimb joints followed a similar pattern for increased cartilage damage. There were statistically significant differences between each joint condition progressively (generalised linear mixed model, P<0.001) in limbs with in vitro cartilage damage of varying severity while the control limbs did not show any changes.

Main limitations: Small sample size using in vitro, mechanically induced cartilage damage.

Conclusion: The AE technique presented here could differentiate the severity of fetlock joint cartilage damage. The consistent results for each simulated condition suggest there is potential for this method in the diagnosis of OA.