Osteoporosis Diagnosis Based on Ultrasound Radio Frequency Signal via Multi-channel Convolutional Neural Network

Zhiwei Chen; Wenqiang Luo; Qi Zhang; Baiying Lei; Tianfu Wang; Zhong Chen; Yuan Fu; Peidong Guo; Changchuan Li; Teng Ma; Yue Ding; Jiang Liu

doi:10.1109/EMBC46164.2021.9629546

Osteoporosis Diagnosis Based on Ultrasound Radio Frequency Signal via Multi-channel Convolutional Neural Network

Zhiwei Chen
, Wenqiang Luo
, Qi Zhang
, Baiying Lei
, Tianfu Wang
, Zhong Chen
, Yuan Fu
, Peidong Guo
, Changchuan Li
, Teng Ma
, Yue Ding
, Jiang Liu^*

^*Corresponding author for this work

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

10 Citations (Scopus)

Abstract

Osteoporosis is a metabolic osteopathy syndrome, and the incidence of osteoporosis increases significantly with age. Currently, bone quantitative ultrasound (QUS) has been considered as a potential method for screening and diagnosing osteoporosis. However, its diagnostic accuracy is quite low. By contrast, deep learning based methods have shown the great power for extracting the most discriminative features from complex data. To improve the osteoporosis diagnostic accuracy and take advantages of QUS, we devise a deep learning method based on ultrasound radio frequency (RF) signal. Specifically, we construct a multi-channel convolutional neural network (MCNN) combined with a sliding window scheme, which can enhance the number of data as well. By using speed of sound (SOS), the quantitative experimental results of our preliminary study indicate that our proposed osteoporosis diagnosis method outperforms the conventional ultrasound methods, which may assist the clinician for osteoporosis screening.

Original language	English
Title of host publication	43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	832-835
Number of pages	4
ISBN (Electronic)	9781728111797
DOIs	https://doi.org/10.1109/EMBC46164.2021.9629546
Publication status	Published - 2021
Externally published	Yes
Event	43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 - Virtual, Online, Mexico Duration: 1 Nov 2021 → 5 Nov 2021

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2021-January
ISSN (Print)	1557-170X

Conference

Conference	43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Country/Territory	Mexico
City	Virtual, Online
Period	1/11/21 → 5/11/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC46164.2021.9629546

Cite this

Chen, Z., Luo, W., Zhang, Q., Lei, B., Wang, T., Chen, Z., Fu, Y., Guo, P., Li, C., Ma, T., Ding, Y., & Liu, J. (2021). Osteoporosis Diagnosis Based on Ultrasound Radio Frequency Signal via Multi-channel Convolutional Neural Network. In 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 (pp. 832-835). (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2021-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC46164.2021.9629546

Chen, Zhiwei ; Luo, Wenqiang ; Zhang, Qi et al. / Osteoporosis Diagnosis Based on Ultrasound Radio Frequency Signal via Multi-channel Convolutional Neural Network. 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 832-835 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

@inproceedings{31ac88ee12344e649068ab39164a1ffe,

title = "Osteoporosis Diagnosis Based on Ultrasound Radio Frequency Signal via Multi-channel Convolutional Neural Network",

abstract = "Osteoporosis is a metabolic osteopathy syndrome, and the incidence of osteoporosis increases significantly with age. Currently, bone quantitative ultrasound (QUS) has been considered as a potential method for screening and diagnosing osteoporosis. However, its diagnostic accuracy is quite low. By contrast, deep learning based methods have shown the great power for extracting the most discriminative features from complex data. To improve the osteoporosis diagnostic accuracy and take advantages of QUS, we devise a deep learning method based on ultrasound radio frequency (RF) signal. Specifically, we construct a multi-channel convolutional neural network (MCNN) combined with a sliding window scheme, which can enhance the number of data as well. By using speed of sound (SOS), the quantitative experimental results of our preliminary study indicate that our proposed osteoporosis diagnosis method outperforms the conventional ultrasound methods, which may assist the clinician for osteoporosis screening.",

author = "Zhiwei Chen and Wenqiang Luo and Qi Zhang and Baiying Lei and Tianfu Wang and Zhong Chen and Yuan Fu and Peidong Guo and Changchuan Li and Teng Ma and Yue Ding and Jiang Liu",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 ; Conference date: 01-11-2021 Through 05-11-2021",

year = "2021",

doi = "10.1109/EMBC46164.2021.9629546",

language = "English",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "832--835",

booktitle = "43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021",

address = "United States",

}

Chen, Z, Luo, W, Zhang, Q, Lei, B, Wang, T, Chen, Z, Fu, Y, Guo, P, Li, C, Ma, T, Ding, Y & Liu, J 2021, Osteoporosis Diagnosis Based on Ultrasound Radio Frequency Signal via Multi-channel Convolutional Neural Network. in 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2021-January, Institute of Electrical and Electronics Engineers Inc., pp. 832-835, 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021, Virtual, Online, Mexico, 1/11/21. https://doi.org/10.1109/EMBC46164.2021.9629546

Osteoporosis Diagnosis Based on Ultrasound Radio Frequency Signal via Multi-channel Convolutional Neural Network. / Chen, Zhiwei; Luo, Wenqiang; Zhang, Qi et al.
43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 832-835 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2021-January).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Osteoporosis Diagnosis Based on Ultrasound Radio Frequency Signal via Multi-channel Convolutional Neural Network

AU - Chen, Zhiwei

AU - Luo, Wenqiang

AU - Zhang, Qi

AU - Lei, Baiying

AU - Wang, Tianfu

AU - Chen, Zhong

AU - Fu, Yuan

AU - Guo, Peidong

AU - Li, Changchuan

AU - Ma, Teng

AU - Ding, Yue

AU - Liu, Jiang

PY - 2021

Y1 - 2021

N2 - Osteoporosis is a metabolic osteopathy syndrome, and the incidence of osteoporosis increases significantly with age. Currently, bone quantitative ultrasound (QUS) has been considered as a potential method for screening and diagnosing osteoporosis. However, its diagnostic accuracy is quite low. By contrast, deep learning based methods have shown the great power for extracting the most discriminative features from complex data. To improve the osteoporosis diagnostic accuracy and take advantages of QUS, we devise a deep learning method based on ultrasound radio frequency (RF) signal. Specifically, we construct a multi-channel convolutional neural network (MCNN) combined with a sliding window scheme, which can enhance the number of data as well. By using speed of sound (SOS), the quantitative experimental results of our preliminary study indicate that our proposed osteoporosis diagnosis method outperforms the conventional ultrasound methods, which may assist the clinician for osteoporosis screening.

AB - Osteoporosis is a metabolic osteopathy syndrome, and the incidence of osteoporosis increases significantly with age. Currently, bone quantitative ultrasound (QUS) has been considered as a potential method for screening and diagnosing osteoporosis. However, its diagnostic accuracy is quite low. By contrast, deep learning based methods have shown the great power for extracting the most discriminative features from complex data. To improve the osteoporosis diagnostic accuracy and take advantages of QUS, we devise a deep learning method based on ultrasound radio frequency (RF) signal. Specifically, we construct a multi-channel convolutional neural network (MCNN) combined with a sliding window scheme, which can enhance the number of data as well. By using speed of sound (SOS), the quantitative experimental results of our preliminary study indicate that our proposed osteoporosis diagnosis method outperforms the conventional ultrasound methods, which may assist the clinician for osteoporosis screening.

UR - https://www.scopus.com/pages/publications/85122504517

U2 - 10.1109/EMBC46164.2021.9629546

DO - 10.1109/EMBC46164.2021.9629546

M3 - Conference contribution

C2 - 34891419

AN - SCOPUS:85122504517

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

SP - 832

EP - 835

BT - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021

Y2 - 1 November 2021 through 5 November 2021

ER -

Chen Z, Luo W, Zhang Q, Lei B, Wang T, Chen Z et al. Osteoporosis Diagnosis Based on Ultrasound Radio Frequency Signal via Multi-channel Convolutional Neural Network. In 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 832-835. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC46164.2021.9629546

Osteoporosis Diagnosis Based on Ultrasound Radio Frequency Signal via Multi-channel Convolutional Neural Network

Abstract

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Conference

UN SDGs

ASJC Scopus subject areas

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