Research on robot-assisted puncture accuracy prediction method based on improved multioutputregressor algorithm

Chuyan Ye; Jianguo Duan; Yao Wang

doi:10.1117/12.3036497

21 July 2024 Research on robot-assisted puncture accuracy prediction method based on improved multioutputregressor algorithm

Chuyan Ye, Jianguo Duan, Yao Wang

Author Affiliations +

Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 1321949 (2024) https://doi.org/10.1117/12.3036497
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China

Abstract

With the rapid development of artificial intelligence, robot-assisted puncture surgery plays an increasingly important role in the medical field. In order to make it clear whether the needle insertion status of the puncture needle is accurate before surgery, this paper proposes a new multi-objective algorithm, i.e., improved MOR. Based on this algorithm, the study uses the optimal parameters of the Random Forest and combines the input features. It can simultaneously predict the puncture needle peak force and the degree of needle tip deflection or offset. In this study, the parameters with optimal prediction results are selected. Compared to existing prediction models in the literature, this developed prediction algorithm is able to predict multiple targets simultaneously. It helps to improve the accuracy and safety of the procedure, thus providing a more accurate guide for the puncture procedure and helping the surgeon to make a preoperative surgical plan.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chuyan Ye, Jianguo Duan, and Yao Wang "Research on robot-assisted puncture accuracy prediction method based on improved multioutputregressor algorithm", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 1321949 (21 July 2024); https://doi.org/10.1117/12.3036497

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