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

Simple Summary Many of the rules considered valid in the ambit of lameness detection in domestic animals are mainly subjective or acquired after extended clinical experience. Thus, it is necessary to develop tools and/or technologies to provide objective data to discern between sound and lame animals. The main objective of this study is to develop an artificial neural network by analyzing data obtained from an inertial sensor in order to discriminate between sound and lame dogs. After different system adjustments, the neural network has been able to correctly determine whether dogs were lame in about 86% of cases. Furthermore, a web app was developed to manage and follow the different cases. An artificial neural network was designed and developed to analyze spatial data from inertial sensors and to detect motion alterations in dogs with unilateral lameness. Displayed within a user-friendly, intuitive web app, the system could be a useful tool for lameness detection for veterinary clinicians. 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.