Orthotic Prescription for Pediatric Flexible Flat Feet using Convolutional Neural Networks

Abstract

Pediatric flexible flat foot (PFFF) is known to increase the foot structure's load, causing potential disability. Foot orthoses are one of the most common non-surgical methods to improve the medial longitudinal arch of the foot for improving PFFF. However, orthoses are not routinely prescribed due to their high cost, and discomfort caused by a restriction of foot movement. Furthermore, there are no quantitative standards or guidelines for an orthotic prescription, which makes the decision-making process of less experienced podiatrists challenging. In this study, the authors investigated convolutional neural networks to classify the needs of orthotic prescription. Using image augmentation techniques and training a VGG-16 model, we achieved high precision and recall, 1 and 0.969 accordingly, to classify orthotic prescription needs.