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使用支持向量机对骨质脱矿进行超声表征

Ultrasound Characterization of Bone Demineralization Using a Support Vector Machine.

作者信息

Denis Max, Wan Leighton, Fatemi Mostafa, Alizad Azra

机构信息

Department of Radiology, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.

Department of Bioengineering, Stanford University, Stanford, California, USA.

出版信息

Ultrasound Med Biol. 2018 Mar;44(3):714-725. doi: 10.1016/j.ultrasmedbio.2017.11.004. Epub 2017 Dec 25.

Abstract

We propose an ultrasound-guided remote measurement technique, utilizing an acoustic radiation force beam as our excitation source and a receiving hydrophone, to assess non-invasively a bone's mechanical properties. Features, such as velocity, were extracted from the acoustic pressure received from the bone surface. The typical velocity of an intact bone (3540 m/s) was higher in comparison to that of a demineralized bone (2231 m/s). According to the receiver operating characteristic curve, the optimal velocity cutoff value of ≥3096 m/s yields 80% sensitivity and 82.61% specificity between intact and demineralized bone. Utilizing a support vector machine, the hours of bone demineralization were successfully classified with maximum accuracy >80% using 18% training data. The results indicate the potential application of our proposed technique and support vector machine for monitoring bone mechanical properties.

摘要

我们提出了一种超声引导下的远程测量技术,利用声辐射力束作为激发源和接收水听器,以非侵入性方式评估骨骼的力学性能。从骨表面接收的声压中提取诸如速度等特征。完整骨骼的典型速度(3540米/秒)比脱矿质骨骼的速度(2231米/秒)更高。根据接收器操作特性曲线,≥3096米/秒的最佳速度截止值在完整骨骼和脱矿质骨骼之间产生80%的灵敏度和82.61%的特异性。利用支持向量机,使用18%的训练数据成功地以大于80%的最大准确率对骨骼脱矿质的时长进行了分类。结果表明我们提出的技术和支持向量机在监测骨骼力学性能方面的潜在应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60d6/5801199/48ba92f954d5/nihms921735f1.jpg

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Ultrasound Characterization of Bone Demineralization Using a Support Vector Machine.使用支持向量机对骨质脱矿进行超声表征
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