View in EDS PDF Full Text

Algorithm-Based Optimization of Taylor Spatial Frame Adjustments for Improved Tibial Deformity Correction.

Saved in:
Bibliographic Details
Title: Algorithm-Based Optimization of Taylor Spatial Frame Adjustments for Improved Tibial Deformity Correction.
Authors: Liu, Tao1,2 liutao@ylnu.edu.cn, Qi, Xiaolong1,2 qixl@ylnu.edu.cn, Lu, Yonghua3 nuaa_lyh@nuaa.edu.cn, Di, Xinyu3 dixinyu@nuaa.edu.cn, Zhang, Yu4 893636863@qq.com
Source: International Journal of Online & Biomedical Engineering. 2026, Vol. 22 Issue 5, p109-123. 15p.
Subjects: Optimization algorithms, Ant algorithms, Pain management, Orthopedic surgery, External fixators, Multi-objective optimization
Abstract: The Taylor spatial frame (TSF) provides minimal surgical trauma during installation, convenient adjustment, and the ability to perform multi-plane correction simultaneously. These advantages have led to its widespread adoption in trauma orthopedics and reconstruction. In conventional TSF-assisted correction, the non-linear coupling between the six struts often causes unintended platform deviations, leading to bone--tissue collision and patient discomfort. This study proposes an algorithmic optimization method combining multi-objective genetic algorithm (MOGA) and ant colony optimization (ACO) to reduce such deviations and improve correction precision. The TSF correction process integrates intelligent algorithms with a specially designed fitness function to optimize the correction plan. Experimental results show that the proposed method effectively reduces TSF deviations during the correction process. This reduction minimizes the risk of bone collisions with surrounding tissues and alleviates patient pain during the procedure. The method's clinical value was further validated by aiding the treatment process in a tibial deformity correction case. The proposed method enhanced the linearity of TSF movement, thereby alleviating patient discomfort during the correction process. This approach shows promising potential for TSF clinical treatment. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Online & Biomedical Engineering is the property of International Journal of Online Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:The Taylor spatial frame (TSF) provides minimal surgical trauma during installation, convenient adjustment, and the ability to perform multi-plane correction simultaneously. These advantages have led to its widespread adoption in trauma orthopedics and reconstruction. In conventional TSF-assisted correction, the non-linear coupling between the six struts often causes unintended platform deviations, leading to bone--tissue collision and patient discomfort. This study proposes an algorithmic optimization method combining multi-objective genetic algorithm (MOGA) and ant colony optimization (ACO) to reduce such deviations and improve correction precision. The TSF correction process integrates intelligent algorithms with a specially designed fitness function to optimize the correction plan. Experimental results show that the proposed method effectively reduces TSF deviations during the correction process. This reduction minimizes the risk of bone collisions with surrounding tissues and alleviates patient pain during the procedure. The method's clinical value was further validated by aiding the treatment process in a tibial deformity correction case. The proposed method enhanced the linearity of TSF movement, thereby alleviating patient discomfort during the correction process. This approach shows promising potential for TSF clinical treatment. [ABSTRACT FROM AUTHOR]
ISSN:26268493
DOI:10.3991/ijoe.v22i05.59611