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基于卷积神经网络的儿童柔性平足矫形器处方

Orthotic Prescription for Pediatric Flexible Flat Feet using Convolutional Neural Networks.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1105-1108. doi: 10.1109/EMBC48229.2022.9871698.

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.

摘要

小儿柔性平足症(PFFF)会增加足部结构的负荷,导致潜在的残疾。足部矫形器是改善 PFFF 中足部内侧纵弓的最常见非手术方法之一。然而,由于矫形器费用高,以及对足部运动的限制导致的不适,矫形器并未常规使用。此外,由于没有矫形器处方的定量标准或指南,经验较少的足病医生的决策过程具有挑战性。在这项研究中,作者研究了卷积神经网络来分类矫形器处方的需求。通过使用图像增强技术和训练 VGG-16 模型,我们实现了高精度和高召回率,分别为 1 和 0.969,以分类矫形器处方的需求。

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