Osteoporosis Prescreening and Bone Mineral Density Prediction using Dental Panoramic Radiographs
Osteoporosis Prescreening and Bone Mineral Density Prediction using Dental Panoramic Radiographs
复制标题
使用牙科全景X光片进行骨质疏松症预筛查和骨矿物质密度预测
DOI：
10.1109/embc46164.2021.9630183
复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
Ling, Haibin
中科院分区：
文献类型：
--
作者：
Singh, Yasha;Atulkar, Vivek;Ren, Jiaxiang;Yang, Jie;Fan, Heng;Latecki, Longin Jan;Ling, Haibin
最近的研究表明，牙科全景X线摄影（DPR）图像有很大的潜力，预先筛选骨质疏松症的骨密度和骨小梁结构之间的高度相关性。在这方面的研究工作中，大多数都是使用预训练的模型进行特征提取和分类，取得了很好的效果。然而，当数据集的大小有限时，使用这些预训练的网络并获得高置信度分数变得困难。在本文中，我们评估了基于深度卷积神经网络（DCNN）的计算机辅助诊断（CAD）系统在全景X光片上检测骨质疏松症的诊断性能，通过与口腔颌面放射科医生的诊断进行比较。利用70张图像的可用标记数据集，重现了初步研究模型的结果。此外，使用不同的计算机视觉技术提高模型的性能。具体而言，利用每个患者的年龄Meta数据来获得更准确的预测。最后，我们试图利用这些图像、年龄和骨密度标签来创建基于神经网络的回归模型，并预测每个患者的骨密度（BMD）值。实验结果表明，建议的CAD系统是在高雅阁有经验的口腔颌面放射科医生在检测骨质疏松症，并达到87.86%的准确率。临床relevance-本文提出了一种方法来检测骨质疏松症使用DPR图像和年龄数据与多列DCNN，然后利用这些数据来预测骨密度为每个病人。
Recent studies have shown that Dental Panoramic Radiograph (DPR) images have great potential for prescreening of osteoporosis given the high degree of correlation between the bone density and trabecular bone structure. Most of the research works in these area had used pretrained models for feature extraction and classification with good success. However, when the size of the data set is limited it becomes difficult to use these pretrained networks and gain high confidence scores. In this paper, we evaluated the diagnostic performance of deep convolutional neural networks (DCNN)based computer-assisted diagnosis (CAD) system in the detection of osteoporosis on panoramic radiographs, through a comparison with diagnoses made by oral and maxillofacial radiologists. With the available labelled dataset of 70 images, results were reproduced for the preliminary study model. Furthermore, the model performance was enhanced using different computer vision techniques. Specifically, the age meta data available for each patient was leveraged to obtain more accurate predictions. Lastly, we tried to leverage these images, ages and osteoporotic labels to create a neural network based regression model and predict the Bone Mineral Density (BMD) value for each patient. Experimental results showed that the proposed CAD system was in high accord with experienced oral and maxillofacial radiologists in detecting osteoporosis and achieved 87.86% accuracy.Clinical relevance— This paper presents a method to detect osteoporosis using DPR images and age data with multi-column DCNN and then leverage this data to predict Bone Mineral Density for each patient.
影响因子：
2.7
作者：
Kavitha MS;Asano A;Taguchi A;Kurita T;Sanada M
通讯作者：
Sanada M
第61章-骨质疏松症
DOI：
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发表时间：
2013
期刊：
影响因子：
--
作者：
J. Cauley
通讯作者：
J. Cauley