Application of Innovative Artificial Intelligence Methods to Detect Flat Feet in Children

This study examined the potential of artificial intelligence tools for detecting pediatric flatfoot pathology. We would like to emphasize that there is very little research in this area and we would like to point out that this is a relevant and very important topic in medicine. First, the base flow was used: a pre-trained “backbone” on the ImageNet platform. In this study, this term is used to describe the feature extraction part of a convolutional network. A standardized pre-processing with pruning and augmentation was performed, and a three-stage training schedule (stages 1, 2 and 3), average and maximum aggregation at the subject level and the addition of light test time were proposed. Eight different model architectures were used. From stage 2 onwards, all models were trained on feet. Three-dimensional photographs with real flatfoot shapes, from flatfoot stages I to III, were used. The most validated model was displayed in accurate AUROC plots with estimated average and maximum aggregation values with standard deviation. The research and calculations conducted demonstrate the possibility of applying artificial intelligence in the field of orthopedics.