Genetic algorithm-based optimization for the geometric design of a novel orthopedic implant
Genetic algorithm-based optimization for the geometric design of a novel orthopedic implant
复制标题
基于遗传算法的新型骨科植入物几何设计优化
DOI：
10.1109/tbme.2021.3080226
复制
发表时间：
2021
影响因子：
4.6
通讯作者：
Balasubramanian, Ravi
中科院分区：
文献类型：
--
作者：
You, Won Suk;Casebier, Justin;Mandich, Jacob A;Balasubramanian, Ravi
ObjectiveA tendon-transfer is a reconstructive orthopedic surgery where tendons are re-routed from a non-functioning muscle and attached a functioning muscle. Prior work has shown that using a passive implanted device in the ECRL-to-FDP tendon-transfer surgery significantly improves hand grasping function. However, it is still unclear how hand-function improvement, measured by finger joint range of motion and torque, is dependent on the implant’s geometry and location within the tendon network. This paper presents a genetic algorithm that determines the device’s optimal geometry and location.MethodsHand biomechanical simulation platform was developed to model hand function and also model the tendon-transfer surgery with and without the implant. Finger kinematics and joint torque were used to develop three unique objective functions to optimize the implant’s parameters.ResultsThe optimized device resulted in an 11X increase in finger kinematics with only a 0.9% decrease in joint torque when compared with the biomechanical function enabled by the current suture-based surgery.ConclusionDesigning implantable devices that modify musculoskeletal function is challenging. Factors like tendon routing and joint kinematics create a complex nonlinear system when considering biomechanical function. A genetic algorithm is an effective tool to tackle these nonlinear landscapes to produce optimized designs.SignificanceThe state-of-the-art surgical procedure to repair high median-ulnar nerve palsy leads to poor hand function and severely limits the patient’s ability to perform activities of daily life. This work provides a method for defining relevant objective functions for hand biomechanical function and then uses those objective functions with genetic algorithms to optimize the geometry of an orthopedic implant across multiple variables. The achieved biomechanical function is significantly better than hand function enabled by current surgical procedure.
ObjectiveA tendon-transfer is a reconstructive orthopedic surgery where tendons are re-routed from a non-functioning muscle and attached a functioning muscle. Prior work has shown that using a passive implanted device in the ECRL-to-FDP tendon-transfer surgery significantly improves hand grasping function. However, it is still unclear how hand-function improvement, measured by finger joint range of motion and torque, is dependent on the implant’s geometry and location within the tendon network. This paper presents a genetic algorithm that determines the device’s optimal geometry and location.MethodsHand biomechanical simulation platform was developed to model hand function and also model the tendon-transfer surgery with and without the implant. Finger kinematics and joint torque were used to develop three unique objective functions to optimize the implant’s parameters.ResultsThe optimized device resulted in an 11X increase in finger kinematics with only a 0.9% decrease in joint torque when compared with the biomechanical function enabled by the current suture-based surgery.ConclusionDesigning implantable devices that modify musculoskeletal function is challenging. Factors like tendon routing and joint kinematics create a complex nonlinear system when considering biomechanical function. A genetic algorithm is an effective tool to tackle these nonlinear landscapes to produce optimized designs.SignificanceThe state-of-the-art surgical procedure to repair high median-ulnar nerve palsy leads to poor hand function and severely limits the patient’s ability to perform activities of daily life. This work provides a method for defining relevant objective functions for hand biomechanical function and then uses those objective functions with genetic algorithms to optimize the geometry of an orthopedic implant across multiple variables. The achieved biomechanical function is significantly better than hand function enabled by current surgical procedure.