Ntelligent Dental Implant Positioning Algorithm Based On Quantum Computing

In surgical implant, implant positioning is done by clinicians based on their own experience with two-dimensional medical imaging. Therefore, it is easy to bring human subjectivity error, which greatly affect the success rate of surgery. With the development of computer technology and artificial intelligence techniques, it is possible to solve combinatorial optimization problems and NP-hard problem. The intelligent algorithm is applied, in order to improve accuracy and intelligence of implant positioning and to reduce the cost of surgery and the patient’s injury.In this thesis, the oral implants intelligent positioning has been studied. In order to improve the success rate of the implant surgery and its degree of intelligence, in-depth analysis and research on quantum genetic algorithm in the positioning of dental implants are discussed. Through some transformation, the complex problems of oral implant positioning, which is taken as a multi-objective and multi-constrained problem, is looked as a high-dimensional optimization, with the parameters of the implant positioning model and its relationship with implant stability considered. With the optimizing operations and strategies, application of quantum genetic algorithm in implant positioning is shown. The fallowings are the major innovation of this article:1) According to the size of three-dimensional medical imaging data and the complexity of preoperative planning, the image processing is applied to filter the medical imaging data. Then the searching space of the intelligent algorithm and constraint conditions are generated.2) With the characteristics of the dental implant position and the case of the successful implant studied, the implant positioning model is proposed. And the fitness function based on this model can effectively optimize the problem of planting positioning in the full mouth. Because of a common ability of the implant positioning model, it can effectively reduce the optimal complexity.3) As the detail implant characters and its effects on the optimization are counted, the evolutionary population is divided into many different sub-populations, and individuals that belong to the same sub-population take the evolutionary operation with the type of adaptive factor and misalignment cross operation.