Artificial intelligence-guided inverse design of deployable thermo-metamaterial implants

Jiao, Pengchen, Zhang, Chenjie, Meng, Wenxuan, Wang, Jiajun, Jang, Daeik, Wu, Zhangming ORCID: https://orcid.org/0000-0001-7100-3282, Agarwal, Nitin and Alavi, Amir H. 2025. Artificial intelligence-guided inverse design of deployable thermo-metamaterial implants. ACS Applied Materials & Interfaces 17 (2) , pp. 2991-3001. 10.1021/acsami.4c17625

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Abstract

Current limitations in implant design often lead to trade-offs between minimally invasive surgery and achieving the desired post-implantation functionality. Here, we present an artificial intelligence inverse design paradigm for creating deployable implants as planar and tubular thermal mechanical metamaterials (thermo-metamaterials). These thermo metamaterial implants exhibit tunable mechanical properties and volume change in response to temperature changes, enabling minimally invasive and personalized surgery. We begin by generating a large database of corrugated thermo-metamaterials with various cell structures and bending stiffnesses. An artificial intelligence inverse design model is subsequently developed by integrating an evolutionary algorithm with a neural network. This model allows for the automatic determination of the optimal microstructure for thermo-metamaterials with desired performance,i.e., target bending stiffness. We validate this approach by designing patient-specific spinal fusion implants and tracheal stents. The results demonstrate that the deployable thermo-metamaterial implants can achieve over a 200% increase in volume or cross-sectional area in their fully deployed states. Finally, we propose a broader vision for a clinically informed artificial intelligence design process that prioritizes biocompatibility, feasibility, and precision simultaneously for the development of high-performing and clinically viable implants. The feasibility of this proposed vision is demonstrated using a fuzzy analytic hierarchy process to customize thermo-metamaterial implants based on clinically relevant factors.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Publisher:	American Chemical Society
ISSN:	1944-8244
Date of First Compliant Deposit:	9 January 2025
Date of Acceptance:	23 December 2024
Last Modified:	16 Jan 2025 11:15
URI:	https://orca.cardiff.ac.uk/id/eprint/175172

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