Canine gait analysis and diagnosis using artificial neural networks and ground reaction force
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Artificial neural networks (ANNs) were developed to map ground reaction force (GRF) data to subjective diagnostic scores of lameness. Twenty-one clinically normal dogs (19–32.2 kg) underwent surgery inducing osteoarthritis in the left hind stifle joint. Lameness scores were assigned by a veterinarian and GRF data were collected twice prior to and five times after the surgery. The study discussed herein focused on identifying the preferred ANN architecture and input variables extracted from GRF curves. The data were partitioned to allow the accuracy of the resulting models to be evaluated with dogs not included in model development. The results indicate that backpropagation neural networks are preferable to probabilistic neural networks. Input variables were identified in this study that capture a dog’s attempt to remove weight from an injured limb. ANNs differentiated the three classes of lameness with an accuracy ranging from 87.8–100%.