| 有监督学习的超声背散射方法在骨质评价中的应用 |
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| 引用本文： | 第五强强, 李博艺, 李颖, 徐峰, 刘成成, 他得安. 有监督学习的超声背散射方法在骨质评价中的应用[J]. 声学学报, 2019, 44(5): 818-825. DOI: 10.15949/j.cnki.0371-0025.2019.05.002 |
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| 作者姓名： | 第五强强 李博艺 李颖 徐峰 刘成成 他得安 |
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| 作者单位： | 1. 复旦大学电子工程系 上海 200433; |
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| 基金项目： | 国家自然科学基金项目(11525416,11604054,11827808,11874289)上海市科技重大专项项目(2017SHZDZX01)资助 |
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| 摘 要： | 在超声背散射方法评价骨质的实际应用中,如何更为准确地判断测量对象是否为骨质疏松是一个重要问题。提出一种有监督学习的超声背散射评价方法,根据超声背散射离体实验的信号处理结果,对松质骨样本使用支撑向量机和自适应增强的有监督学习算法进行预测和分类。研究结果表明,有监督学习的超声背散射评价方法分类的准确率为80.00%~82.86%,并且对骨质疏松的样本具有较高的特异性(特异度>92.3%)。因此有监督学习的超声背散射评价方法具有有效性,评价效果优于现有的其它定量超声方法,对超声背散射方法的在体应用有一定帮助。
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| 关 键 词： | 骨质疏松 松质骨 超声背散射 有监督学习 |
| 收稿时间： | 2018-01-17 |
| 修稿时间： | 2019-01-02 |
Application of supervised ultrasonic backscattering measurement in bone evaluation |
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| DIWU Qiangqiang, LI Boyi, LI Ying, XU Feng, LIU Chengcheng, TA Dean. Application of supervised ultrasonic backscattering measurement in bone evaluation[J]. ACTA ACUSTICA, 2019, 44(5): 818-825. DOI: 10.15949/j.cnki.0371-0025.2019.05.002 |
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| Authors: | DIWU Qiangqiang LI Boyi LI Ying XU Feng LIU Chengcheng TA Dean |
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| Affiliation: | 1. Department of Electronic Engineering, Fudan University, Shanghai 200433;2. Institute of Acoustics, Tongji University, Shanghai 200092 |
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| Abstract: | In the practical application of ultrasonic backscattering measurement,it is essential that how to determine more accurately whether the measured object is osteoporosis.Based on the signal processing results of ultrasonic backscattering experiment,cancellous bone specimens were predicted and classified by supervised support vector machine and adaptive boost algorithms.The results showed that the accuracy of classification was 80.00%-82.86%,and the specificity of osteoporosis was significant(specificity>92.3%).Therefore,the supervised ultrasonic backscattering method is effective and its performance is superior to other existing quantitative ultrasonic methods.It is helpful to the ultrasonic backscattering measurement in vivo. |
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| Keywords: | Osteoporosis Cancellous bone Ultrasonic backscattering Supervised machine learning |
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