Osteoporosis Diagnostic Model Using a Multichannel Convolutional Neural Network Based on Quantitative Ultrasound Radiofrequency Signal |
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| Institution: | 1. Department of Orthopedic Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China;2. Bioland Laboratory, Guangzhou, China;3. National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China;4. Paul C. Lauterbur Research Center for Biomedical Imaging, Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Key Laboratory of Ultrasound Imaging and Therapy, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;5. Cixi Institute of Biomedical Engineering, Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Ningbo, China;6. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China |
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| 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. |
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