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Predicting the effective compressive modulus of human cancellous bone using the conventional neural network method

Lu, Yongtao, Yang, Zhuoyue, Zhu, Hanxing ORCID: and Wu, Chengwei 2023. Predicting the effective compressive modulus of human cancellous bone using the conventional neural network method. Computer Methods in Biomechanics and Biomedical Engineering 26 (10) , pp. 1150-1159. 10.1080/10255842.2022.2112183

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The efficient prediction of biomechanical properties of bone plays an important role in the assessment of bone quality. However, the present techniques are either of low accuracy or of high complexity for the clinical application. The present study aims to investigate the predictive ability of the evolving convolutional neural network (CNN) technique in predicting the effective compressive modulus of porous bone structures. The T11/T12/L1 segments of thirty-five female cadavers were scanned using the HR-pQCT scanner and the images obtained from it were used to generate 10896 2 D bone samples, in which only the cancellous bony parts were processed and investigated. The corresponding 10896 heterogeneous finite-element (FE) models were generated, and then a CNN model was constructed and trained using the predictions of the FE analysis as the ground truths. Then the remaining 260 bone samples generated from the initial HR-pQCT images were used to test the predictive power of the CNN model. The results show that the coefficient of the determinant (R2) from the linear correlation between the CNN and FE predicted elastic modulus is 0.95, which is much higher than that from the correlation between the BMD and the FE predictions (R2 = 0.65). Furthermore, the 95th and 50th percentiles of relative prediction error are below 0.28 and 0.09, respectively. In the conclusion, the CNN model can efficiently predict the effective compressive modulus of human cancellous bone and can be used as a promising and clinically applicable method to evaluate the mechanical quality of porous bone.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Taylor and Francis
ISSN: 1025-5842
Date of First Compliant Deposit: 17 August 2022
Date of Acceptance: 6 August 2022
Last Modified: 11 Nov 2023 03:16

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