Abstract
Pediatric flexible flat foot (PFFF) is known to in-crease 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 chal-lenging. 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.
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