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Designing anisotropic porous bone scaffolds using a self-learning convolutional neural network model

Lu, Yongtao, Gong, Tingxiang, Yang, Zhuoyue, Zhu, Hanxing ORCID:, Liu, Yadong and Wu, Chengwei 2022. Designing anisotropic porous bone scaffolds using a self-learning convolutional neural network model. Frontiers in Bioengineering and Biotechnology 10 , 973275. 10.3389/fbioe.2022.973275

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The design of bionic bone scaffolds to mimic the behaviors of native bone tissue is crucial in clinical application, but such design is very challenging due to the complex behaviors of native bone tissues. In the present study, bionic bone scaffolds with the anisotropic mechanical properties similar to those of native bone tissues were successfully designed using a novel self-learning convolutional neural network (CNN) framework. The anisotropic mechanical property of bone was first calculated from the CT images of bone tissues. The CNN model constructed was trained and validated using the predictions from the heterogonous finite element (FE) models. The CNN model was then used to design the scaffold with the elasticity matrix matched to that of the replaced bone tissues. For the comparison, the bone scaffold was also designed using the conventional method. The results showed that the mechanical properties of scaffolds designed using the CNN model are closer to those of native bone tissues. In conclusion, the self-learning CNN framework can be used to design the anisotropic bone scaffolds and has a great potential in the clinical application.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Additional Information: This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY)
Publisher: Frontiers Media
ISSN: 2296-4185
Date of First Compliant Deposit: 27 September 2022
Date of Acceptance: 7 September 2022
Last Modified: 05 May 2023 22:08

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