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		题名	作者	年代	出处	被引量
	1	有监督学习的超声背散射方法在骨质评价中的应用显示文摘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。	第五强强李博艺李颖徐峰刘成成他得安	2019	声学学报2019，44，5：	4
	2	Application of a supervised machine learning algorithm in bone evaluation using ultrasonic backscatter signal显示文摘Improving the diagnosis accuracy is essential for the clinical application of osteoporosis evaluation using ultrasonic backscatter signal.In vitro ultrasonic backscatter signals were measured on bone specimens and backscatter parameters were calculated.Using the measured backscatter parameters,the involved cancellous bone specimens were evaluated and classified using support vector machine and adaptive boosting algorithms.Results showed that the accuracy of classification was 80.00%-82.86% and the specificity of osteoporosis diagnosis was significant(specificity>92.3%).The supervised machine learning method using ultrasonic backscatter in bone evaluation is effective in the diagnosis of osteoporosis.The performance of the proposed machine-learning method is superior to the traditional bone evaluation using quantitative backscatter parameters.This study may contribute to the application of ultrasonic backscatter in the diagnosis of osteoporosis in vivo.	DIWU Qiangqiang LIU Chengcheng LI Ying LI Boyi XU Feng TA Dean	2020	Chinese Journal of Acoustics2020，39，3：	0

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