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Detection of osteoporosis from percussion responses using an electronic stethoscope and machine learning

Scanlan, J; Li, FF; Umnova, O; Rakoczy, G; Lövey, N; Scanlan, P

Detection of osteoporosis from percussion responses using an electronic stethoscope and machine learning Thumbnail


Authors

J Scanlan

FF Li

O Umnova

G Rakoczy

N Lövey

P Scanlan



Abstract

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.

Citation

Scanlan, J., Li, F., Umnova, O., Rakoczy, G., Lövey, N., & Scanlan, P. (2018). Detection of osteoporosis from percussion responses using an electronic stethoscope and machine learning. Bioengineering, 5(4), 107. https://doi.org/10.3390/bioengineering5040107

Journal Article Type Article
Acceptance Date Dec 3, 2018
Online Publication Date Dec 5, 2018
Publication Date Dec 5, 2018
Deposit Date Jan 7, 2019
Publicly Available Date Jan 7, 2019
Journal Bioengineering
Publisher MDPI
Volume 5
Issue 4
Pages 107
DOI https://doi.org/10.3390/bioengineering5040107
Keywords classification, electronic stethoscope, impulse response, machine learning, osteoporosis, pattern recognition, resonant frequency, signal processing, vibro-acoustics
Publisher URL https://doi.org/10.3390/bioengineering5040107
Related Public URLs https://www.mdpi.com/journal/bioengineering

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