Real-time optimization of prosthetic design for complete arch implant-supported treatments using finite element-based machine learning.

作者信息

Chen Yung-Chung, Lin Jia-Wei, Wang Kuan-Hsin, Lin Chi-Lun

机构信息

Associate Professor, School of Dentistry & Institute of Oral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan ROC; and Chief, Division of Prosthodontics, Department of Stomatology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan ROC.

Research Assistant, Department of Mechanical Engineering, National Cheng Kung University, Tainan, Taiwan ROC.

出版信息

J Prosthet Dent. 2024 Jul 16. doi: 10.1016/j.prosdent.2024.06.012.

DOI:10.1016/j.prosdent.2024.06.012

PMID:39019724

Abstract

STATEMENT OF PROBLEM

The complete arch implant-supported treatment concept with 2 angled implants has been widely used for the prosthetic rehabilitation of edentulous patients. While mechanical analysis plays a pivotal role in minimizing suboptimal outcomes or premature failure, it is notably time-consuming. Consequently, clinical treatment planning relies heavily on dentists' subjective judgment and an optimization process is needed.

PURPOSE

The purpose of this study was to develop an optimization process for providing immediate recommendations to support decision-making in configuring complete arch implant-supported prostheses.

MATERIAL AND METHODS

This research was carried out in 2 phases. The first consisted of collecting a dataset from a total of 2800 finite element simulations by randomly configuring 10 implant design variables with 4 types of mandibles. The dataset was used to train an artificial neural network to predict the biomechanical performance of a given complete arch implant-supported prosthesis design configuration. In the second phase, the artificial neural network was used as the objective function predictor in a particle swarm optimization process to enable immediate recommendations for the implant placement. The optimization process was evaluated for accuracy, computing performance, and adaptability for unseen mandibles.

RESULTS

Within the specified design space, the optimization process was able to find an optimal design based on an imported mandible model in 30 seconds. The optimized designs were found to improve peri-implant stress by 11.08 ±6.43%. When verified through finite element analysis, the prediction error was found to be 10.4 ±8.1%. Furthermore, the prediction of the optimal design was highly accurate when tested on 2 unseen mandibles, yielding an error of less than 1.7%.

CONCLUSIONS

The suggested approach can quickly provide an optimal implant configuration for each individual and effectively reduce the average peri-implant stress in the mandible.

摘要