Development of an Artificial Neural Network for the Detection of Supporting Hindlimb Lameness: A Pilot Study in Working Dogs

Abstract

Subjective lameness assessment has been a controversial subject given the lack of agreement between observers; this has prompted the development of kinetic and kinematic devices in order to obtain an objective evaluation of locomotor system in dogs. After proper training, neural networks are potentially capable of making a non-human diagnosis of canine lameness. The purpose of this study was to investigate whether artificial neural networks could be used to determine canine hindlimb lameness by computational means only. The outcome of this study could potentially assess the efficacy of certain treatments against diseases that cause lameness. With this aim, input data were obtained from an inertial sensor positioned on the rump. Data from dogs with unilateral hindlimb lameness and sound dogs were used to obtain differences between both groups at walk. The artificial neural network, after necessary adjustments, was integrated into a web management tool, and the preliminary results discriminating between lame and sound dogs are promising. The analysis of spatial data with artificial neural networks was summarized and developed into a web app that has proven to be a useful tool to discriminate between sound and lame dogs. Additionally, this environment allows veterinary clinicians to adequately follow the treatment of lame canine patients.