Personalized prosthesis design in all-on-4® treatment through deep learning-accelerated structural optimization.

Background/purpose: The All-on-4® treatment concept is a dental procedure that utilizes only four dental implants to support a fixed prosthesis, providing full-arch rehabilitation with affordable cost and speedy treatment courses. Although the placement of all-on-4® implants has been researched in the past, little attention was paid to the structural design of the prosthetic framework. Materials and methods: This research proposed a new approach to optimize the structure of denture framework called BESO-Net, which is a bidirectional evolutionary structural optimization (BESO) based convolutional neural network (CNN). The approach aimed to reduce the use of material for the framework, such as Ti-6Al-4V, while maintaining structural strength. The BESO-Net was designed as a one-dimensional CNN based on Inception V3, trained using finite element analysis (FEA) data from 14,994 design configurations, and evaluated its training performance, generalization capability, and computation efficiency. Results: The results suggested that BESO-Net accurately predicted the optimal structure of the denture framework in various mandibles with different implant and load settings. The average error was found to be 0.29% for compliance and 11.26% for shape error when compared to the traditional BESO combined with FEA. Additionally, the computational time required for structural optimization was significantly reduced from 6.5 h to 45 s. Conclusion: The proposed approach demonstrates its applicability in clinical settings to quickly find personalized All-on-4® framework structure that can significantly reduce material consumption while maintaining sufficient stiffness.