Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Material Design of Porous Hydroxyapatite Ceramics via Inverse Analysis of an Estimation Model for Bone-Forming Ability Based on Machine Learning and Experimental Validation of Biological Hard Tissue Responses

Shota Horikawa
,
Kitaru Suzuki
ORCID logo ,
Kohei Motojima
,
Kazuaki Nakano
,
Masaki Nagaya
ORCID logo ,
Hiroshi Nagashima
ORCID logo ,
Hiromasa Kaneko
,
Mamoru Aizawa
* ORCID logo
Version 1 : Received: 26 December 2023 / Approved: 26 December 2023 / Online: 27 December 2023 (07:46:46 CET)

A peer-reviewed article of this Preprint also exists.

Horikawa, S.; Suzuki, K.; Motojima, K.; Nakano, K.; Nagaya, M.; Nagashima, H.; Kaneko, H.; Aizawa, M. Material Design of Porous Hydroxyapatite Ceramics via Inverse Analysis of an Estimation Model for Bone-Forming Ability Based on Machine Learning and Experimental Validation of Biological Hard Tissue Responses. Materials 2024, 17, 571. Horikawa, S.; Suzuki, K.; Motojima, K.; Nakano, K.; Nagaya, M.; Nagashima, H.; Kaneko, H.; Aizawa, M. Material Design of Porous Hydroxyapatite Ceramics via Inverse Analysis of an Estimation Model for Bone-Forming Ability Based on Machine Learning and Experimental Validation of Biological Hard Tissue Responses. Materials 2024, 17, 571.

Abstract

Hydroxyapatite and b-tricalcium phosphate have been clinically applied as artificial bone materials due to their high biocompatibility. The development of artificial bones requires the verification of safety and efficacy through animal experiments; however, from the viewpoint of animal welfare, there is necessary to reduce the number of animal experiments. In this study, we utilized machine learning to construct a model that estimates the bone-forming ability of bioceramics from material fabrication conditions, material properties, and in vivo experimental conditions. We succeeded in constructing the two models as follows: ‘Model 1’, which predicts material properties from their fabrication conditions, and ‘Model 2’, which predicts the bone-formation rate from material properties and in vivo experimental conditions. By an inverse analysis of the two models, we proposed candidates for material fabrication conditions to achieve target values of bone-formation rate. Under the proposed conditions, the material properties of the fabricated material were consistent with the estimated material properties. Furthermore, a comparison between bone-formation rates after 12 weeks of implantation in the porcine tibia and the estimated bone-formation rate. This result showed that the actual bone-formation rates existed within the error range of the estimated bone-formation rates, indicating that machine learning consistently predicts the results of animal experiments using material fabrication conditions. We believe that these findings will lead to the establishment of alternative animal experiments to replace animal experiments in the development of artificial bones.

Keywords

hydorxyapatite; bone-foration rate; machine learning; inverse analysis; experimental validation

Subject

Chemistry and Materials Science, Biomaterials

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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