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A Deep Neural Network-Based Method for Early Detection of Osteoarthritis Using Statistical Data

机译：基于深度神经网络的骨关节炎早期统计方法

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A large number of people suffer from certain types of osteoarthritis, such as knee, hip, and spine osteoarthritis. A correct prediction of osteoarthritis is an essential step to effectively diagnose and prevent severe osteoarthritis. Osteoarthritis is commonly diagnosed by experts through manual inspection of patients’ medical images, which are usually collected in hospitals. Checking the occurrence of osteoarthritis is somewhat time-consuming for patients. In addition, the current studies are focused on automatically detecting osteoarthritis through image-based deep learning algorithms. This needs patients’ medical images, which requires patients to visit the hospital. However, medical utilization and health behavior information as statistical data are easier to collect and access than medical images. Using indirect statistical data without any medical images to predict the occurrence of diverse forms of OA can have significant impacts on pro-active and preventive medical care. In this study, we used a deep neural network for detecting the occurrence of osteoarthritis using patient’s statistical data of medical utilization and health behavior information. The study was based on 5749 subjects. Principal component analysis with quantile transformer scaling was employed to generate features from the patients’ simple background medical records and identify the occurrence of osteoarthritis. Our experiments showed that the proposed method using deep neural network with scaled PCA resulted in 76.8% of area under the curve ( AUC ) and minimized the effort to generate features. Hence, this methos can be a promising tool for patients and doctors to prescreen for possible osteoarthritis to reduce health costs and patients’ time in hospitals.
机译：很多人患有某些类型的骨关节炎，例如膝盖，臀部和脊柱骨关节炎。正确预测骨关节炎是有效诊断和预防严重骨关节炎的重要步骤。骨关节炎通常由专家通过手动检查患者的医学图像来诊断，这些图像通常在医院收集。对患者而言，检查骨关节炎的发生有些耗时。此外，当前的研究集中在通过基于图像的深度学习算法自动检测骨关节炎。这需要患者的医学图像，这需要患者去医院就诊。但是，医学利用率和健康行为信息作为统计数据比医学图像更易于收集和访问。使用没有任何医学图像的间接统计数据来预测OA多种形式的发生可能会对主动和预防性医学护理产生重大影响。在这项研究中，我们使用了深度神经网络，利用患者的医疗利用率和健康行为信息的统计数据来检测骨关节炎的发生。该研究基于5749名受试者。采用分位数变换标度的主成分分析来从患者的简单背景医疗记录中生成特征，并确定骨关节炎的发生。我们的实验表明，所提出的使用带有缩放PCA的深度神经网络的方法可产生曲线下面积（AUC）的76.8％，并最大程度地减少了生成特征的工作。因此，这种方法对于患者和医生来说是一种有前途的工具，可以对可能的骨关节炎进行预筛查，以减少医疗费用和缩短患者在医院的时间。

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