Use of a Multiplex PCR-based Assay to Differentiate Campylobacter jejuni and Campylobacter coli Strains Isolated from Human and Animal Sources

The purpose of this study was to investigate whether artificial neural networks could be used to determine equine lameness by computational means only. The integral parts of our approach were the combination of automated signal tracking of horses on a treadmill and the computational power of artificial neural networks (ANN). The motion of 175 horses trotting on a treadmill was recorded using the SELSPOT II system for motion analysis. Two cameras traced infrared (IR) markers on the head and on the left forehoof. The motion of the head was Fourier-transformed and further processed by a multilayer feedforward ANN, which was trained to distinguish healthy from pathological gaits and to quantify the lameness. The classification was correct in 78.6% of cases. In 12% of cases the network gave contradictory results, in 5.9% the network found no answers, and in 3.5% the answers were wrong. However after proper training, it is proposed that neural networks are potentially capable of making a non-human diagnosis of equine lameness.