Scanlan Jamie, Li Francis F, Umnova Olga, Rakoczy Gyorgy, Lövey Nóra, Scanlan Pascal
The School of Computing, Science & Engineering, Newton Building, University of Salford, Salford, Greater Manchester M5 4WT, UK.
Warrington Hospital, Lovely Lane, Warrington, Cheshire WA5 1QG, UK.
Bioengineering (Basel). 2018 Dec 5;5(4):107. doi: 10.3390/bioengineering5040107.
Osteoporosis is an asymptomatic bone condition that affects a large proportion of the elderly population around the world, resulting in increased bone fragility and increased risk of fracture. Previous studies had shown that the vibroacoustic response of bone can indicate the quality of the bone condition. Therefore, the aim of the authors' project is to develop a new method to exploit this phenomenon to improve detection of osteoporosis in individuals. In this paper a method is described that uses a reflex hammer to exert testing stimuli on a patient's tibia and an electronic stethoscope to acquire the impulse responses. The signals are processed as mel frequency cepstrum coefficients and passed through an artificial neural network to determine the likelihood of osteoporosis from the tibia's impulse responses. Following some discussions of the mechanism and procedure, this paper details the signal acquisition using the stethoscope and the subsequent signal processing and the statistical machine learning algorithm. Pilot testing with 12 patients achieved over 80% sensitivity with a false positive rate below 30% and accuracies in the region of 70%. An extended dataset of 110 patients achieved an error rate of 30% with some room for improvement in the algorithm. By using common clinical apparatus and strategic machine learning, this method might be suitable as a large population screening test for the early diagnosis of osteoporosis, thus avoiding secondary complications.
骨质疏松症是一种无症状的骨骼疾病，影响着全球很大一部分老年人口，导致骨脆性增加和骨折风险升高。先前的研究表明，骨骼的振动声学响应可以表明骨骼状况的质量。因此，作者项目的目的是开发一种新方法，利用这一现象来改善对个体骨质疏松症的检测。本文描述了一种方法，该方法使用反射锤对患者的胫骨施加测试刺激，并使用电子听诊器获取脉冲响应。这些信号被处理为梅尔频率倒谱系数，并通过人工神经网络，根据胫骨的脉冲响应来确定患骨质疏松症的可能性。在对机制和程序进行一些讨论之后，本文详细介绍了使用听诊器进行信号采集以及随后的信号处理和统计机器学习算法。对12名患者进行的初步测试灵敏度超过80%，假阳性率低于30%，准确率在70%左右。110名患者的扩展数据集错误率为30%，该算法仍有改进空间。通过使用常见的临床设备和策略性机器学习，这种方法可能适合作为一种大规模人群筛查测试，用于骨质疏松症的早期诊断，从而避免继发性并发症。
