Department of Mechanical Design Engineering/Major in Materials, Devices, and Equipment, Hanyang University, 222, Wangsimni-ro, Seongdongsu, Seoul, 04763, Republic of Korea; BK21 FOUR ERICA-ACE Center, Hanyang University, Ansan, Gyeonggi, 15588, Republic of Korea.
Department of Orthopaedic Surgery, International St. Mary's Hospital, Catholic Kwandong University College of Medicine, Incheon, 404-834, Republic of Korea.
Comput Biol Med. 2021 May;132:104298. doi: 10.1016/j.compbiomed.2021.104298. Epub 2021 Feb 27.
Osteoporosis, which is a common disorder associated with low bone mineral density (BMD), is one of the primary reasons for hip fracture. It not only limits mobility, but also makes the patient suffer from pain. Unlike traditional methods, which require both expensive equipment and long scanning times, this study aims to develop a novel technique employing a convolutional neural network (CNN) directly on radiographs of the hips to evaluate BMD. To construct the dataset, X-ray photographs of lower limbs and dual-energy X-ray absorptiometry (DXA) results of the hips of patients were collected. The core of this research is a deep learning-based model that was trained using the pre-processed X-rays images of 510 hips as the input data and the BMD values obtained from DXA as the standard reference. To improve performance quality, the radiographs of the hips were processed with a Sobel algorithm to extract the gradient magnitude maps, and an ensemble artificial neural network which analyses the outputs of CNN models corresponding to three Singh sites and biological parameters was utilized. The superior performance of the proposed method was confirmed by the high correlation coefficient of 0.8075 (p<0.0001) of the BMD measured by DXA in a total of 150 testing cases, with only 0.12 s required for applying the computing configuration to a single X-ray image.
骨质疏松症是一种常见的骨骼矿物质密度（BMD）降低相关疾病，是导致髋部骨折的主要原因之一。它不仅限制了活动能力，还使患者遭受疼痛。与传统方法不同，传统方法既需要昂贵的设备又需要长时间的扫描，本研究旨在开发一种新的技术，直接在髋关节的射线照片上使用卷积神经网络（CNN）来评估 BMD。为了构建数据集，收集了下肢的 X 射线照片和髋关节的双能 X 射线吸收法（DXA）结果。该研究的核心是一个基于深度学习的模型，该模型使用 510 个髋关节的预处理 X 射线图像作为输入数据，并使用 DXA 获得的 BMD 值作为标准参考进行训练。为了提高性能质量，使用 Sobel 算法处理髋关节的射线照片以提取梯度幅度图，并利用分析对应于三个 Singh 部位和生物参数的 CNN 模型输出的集成人工神经网络。通过在总共 150 个测试案例中，DXA 测量的 BMD 的相关性系数为 0.8075（p<0.0001），证明了该方法的优越性，仅需 0.12 秒即可将计算配置应用于单个 X 射线图像。
