Osteoporosis Diagnostic Model Using a Multichannel Convolutional Neural Network Based on Quantitative Ultrasound Radiofrequency Signal

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

Research output: Journal PublicationArticlepeer-review

Abstract

Quantitative ultrasound (QUS) is a promising screening method for osteoporosis. In this study, a new method to improve the diagnostic accuracy of QUS was established in which a multichannel convolutional neural network (MCNN) processes the raw radiofrequency (RF) signal of QUS. The improvement in the diagnostic accuracy of osteoporosis using this new method was evaluated by comparison with the conventional speed of sound (SOS) method. Dual-energy X-ray absorptiometry was used as the diagnostic standard. After being trained, validated and tested in a data set consisting of 274 participants, the MCNN model could significantly raise the accuracy of osteoporosis diagnosis compared with the SOS method. The adjusted MCNN model performed even better when adjusted by age, height and weight data. The sensitivity, specificity and accuracy of the adjusted MCNN method for osteoporosis diagnosis were 80.86%, 84.23% and 83.05%, respectively; the corresponding values for SOS were 50.60%, 73.68% and 66.67%. The area under the receiver operating characteristic curve of the adjusted MCNN method was also higher than that of SOS (0.846 vs. 0.679). In conclusion, our study indicates that the MCNN method may be more accurate than the conventional SOS method. The MCNN tool and ultrasound RF signal analysis are promising future developmental directions for QUS in screening for osteoporosis.

Original languageEnglish
Pages (from-to)1590-1601
Number of pages12
JournalUltrasound in Medicine and Biology
Volume48
Issue number8
DOIs
Publication statusPublished - Aug 2022
Externally publishedYes

Keywords

  • Deep learning
  • Osteoporosis diagnosis
  • Quantitative ultrasound
  • Radiofrequency
  • Speed of sound

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Biophysics
  • Acoustics and Ultrasonics

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