Abstract
OBJECTIVE: Chronic constriction injury (CCI) of the sciatic nerve is a peripheral nerve injury widely used to induce mononeuropathy. This study used machine learning methods to identify the best gait analysis parameters for evaluating peripheral nerve injuries. METHODS: Twenty-eight male Wistar rats (weighing 270±10 g), were used in the present study and divided into the following 4 groups: CCI with 4 ligatures around the sciatic nerve (CCI-4L; n=7), a modified CCI model with 1 ligature (CCI-1L; n=7), a sham group (n=7), and a healthy control group (n=7). All rats underwent gait analysis 7 and 28 days postinjury. The data were evaluated using Kinovea and WeKa software (machine learning and neural networks). RESULTS: In the machine learning analysis of the experimental groups, the pre-swing (PS) angle showed the highest ranking in all 3 analyses (sensitivity, specificity, and area under the receiver operating characteristics curve using the Naive Bayes, k-nearest neighbors, radial basis function classifiers). Initial contact (IC), step length, and stride length also performed well. Between 7 and 28 days after injury, there was an increase in the total course time, step length, stride length, stride speed, and IC, and a reduction in PS and IC-PS. Statistically significant differences were found between the control group and experimental groups for all parameters except speed. Interactions between time after injury and nerve injury type were only observed for IC, PS, and IC-PS. CONCLUSION: PS angle of the ankle was the best gait parameter for differentiating nonlesions from nerve injuries and different levels of injury.