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Detection of Osteoporosis from Percussion Responses Using an Electronic Stethoscope and Machine Learning
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 con...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6316453/ https://www.ncbi.nlm.nih.gov/pubmed/30563076 http://dx.doi.org/10.3390/bioengineering5040107 |
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author | Scanlan, Jamie Li, Francis F. Umnova, Olga Rakoczy, Gyorgy Lövey, Nóra Scanlan, Pascal |
author_facet | Scanlan, Jamie Li, Francis F. Umnova, Olga Rakoczy, Gyorgy Lövey, Nóra Scanlan, Pascal |
author_sort | Scanlan, Jamie |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6316453 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-63164532019-01-10 Detection of Osteoporosis from Percussion Responses Using an Electronic Stethoscope and Machine Learning Scanlan, Jamie Li, Francis F. Umnova, Olga Rakoczy, Gyorgy Lövey, Nóra Scanlan, Pascal Bioengineering (Basel) Article 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. MDPI 2018-12-05 /pmc/articles/PMC6316453/ /pubmed/30563076 http://dx.doi.org/10.3390/bioengineering5040107 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Scanlan, Jamie Li, Francis F. Umnova, Olga Rakoczy, Gyorgy Lövey, Nóra Scanlan, Pascal Detection of Osteoporosis from Percussion Responses Using an Electronic Stethoscope and Machine Learning |
title | Detection of Osteoporosis from Percussion Responses Using an Electronic Stethoscope and Machine Learning |
title_full | Detection of Osteoporosis from Percussion Responses Using an Electronic Stethoscope and Machine Learning |
title_fullStr | Detection of Osteoporosis from Percussion Responses Using an Electronic Stethoscope and Machine Learning |
title_full_unstemmed | Detection of Osteoporosis from Percussion Responses Using an Electronic Stethoscope and Machine Learning |
title_short | Detection of Osteoporosis from Percussion Responses Using an Electronic Stethoscope and Machine Learning |
title_sort | detection of osteoporosis from percussion responses using an electronic stethoscope and machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6316453/ https://www.ncbi.nlm.nih.gov/pubmed/30563076 http://dx.doi.org/10.3390/bioengineering5040107 |
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