Study of Multiobjective Genetic Algorithm on Taylor External Fixation

Abstract

The rapid development of today’s society and the rapidly increasing incidence of skeletal injuries caused by traffic and industrial accidents have led to a significant increase in the number of patients with fractures accompanied by severe soft tissue injuries, as well as an increased incidence of osteomyelitis and bone defects. Many of these patients cannot be treated with internal fixators and can only be treated with external fixators. The Taylor Bone External Fixator is the most advanced bone external fixation brace in the field of orthopedics, in which the algorithm study of the forward and backward solution of the computer software that accompanies the Taylor Bone External Fixator is the key. In this paper, we analyze the positional kinematic model based on the Taylor Spatial Frame and derive the equations for solving the six positional parameters of the fracture segment in the dynamic platform with this model. A multi-objective genetic algorithm and Pareto optimization theory are combined to propose a solution to the kinematic positive solution problem. The algorithm proposed in this paper has a minimum accuracy improvement of about 0.8 mm compared to the conventional Newton-Raphson in terms of the accuracy of the Taylor frame mounting parameters solution. Finally, based on the human tibial fracture as the test object, the healing process of the tibial fracture end was simulated using the prescription data generated by the orthopedic system, and the experimental results proved the accuracy and feasibility of the software system, and achieved the expected orthopedic effect.

Keywords

• Taylor bone external fixator
• multi-objective genetic algorithm
• Pearson correlation coefficient
• kinematic positive solution
• correction prescription