| Project/Area Number |
13672181
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
矯正・小児・社会系歯学
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| Research Institution | Fukuoka College of Health Sciences |
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Principal Investigator |
MASUI Ichiro Fukuoka College of Health Sciences, Department of Oral Hygiene, Professor, 歯科衛生学科, 教授 (50131884)
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| Co-Investigator(Kenkyū-buntansha) |
MIKI Tsutomu Kyushu Institute of Technology, Graduate school of Life Science and Systems Engineering, Associate Professor, 大学院・生命体工学研究科, 助教授 (20231607)
YAMAKAWA Takeshi Kyushu Institute of Technology, Graduate school of Life Science and Systems Engineering, Professor, 大学院・生命体工学研究科, 教授 (00005547)
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| Project Period (FY) |
2001 – 2003
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| Project Status |
Completed (Fiscal Year 2003)
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| Budget Amount *help |
¥2,500,000 (Direct Cost: ¥2,500,000)
Fiscal Year 2003: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2002: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2001: ¥1,000,000 (Direct Cost: ¥1,000,000)
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| Keywords | Jaw deformity / Classification of Profile / Planning of orthognathic surgery / Soft computing / Cephalogram / Prediction of postoperative profile / Application software / Graphical Database / 自己組織化マップ / 遺伝的アルゴリズム / ネオファジィニューロン / 側貌予測 / 症型分類 / 術後側貌予測 / ユーザーインターフェイス / ソフトウエア / 外科的矯正治療 / 顎矯正手術 / 手術計画支援システム |
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| Research Abstract |
We developed an artificial intelligent system based on soft computing for planning of orthognathic surgery in surgical correction of jaw deformities.
The self-organizing map neural network (SOM) has a feature of a mapping from a high dimensional space to a low dimensional space with keeping its topology. Accordingly, similar cases are closely located on the map. Using this feature, we applied SOM to classification of soft-tissue profiles of jaw deformities. Materials were pre-and post-operative lateral cephalograms of 44 patients. Soft-tissue profiles were measured and classified (diagnosed) into seven groups. Eleven soft-tissue measurements were selected as input vector of SOM. The genetic algorithm (GA) was employed to optimize coefficients for each measurement. The hierarchical classification method based SOM and GA resulted about 100% corresponding to the previous profile diagnosis.
In planning of orthognathic surgery case, it is meaningful to refer the past documentation similar to the new case. We constructed database software that automatically searches for similar cases from a number of various cases stored by means of SOM. It is also useful to predict the profile after surgery. Profile change is a nonlinear result from the interaction of not only skeletal movement but also soft tissue thickness. Using neo fuzzy neuron that can approximate a nonlinear relationship between inputs and outputs. Comparing prediction errors between the neo frizzy neuron method and the linear multiple regression method, the neo fuzzy neuron method was suitable for prediction of resultant profile after orthognathic surgery. A photograph of the patient's predicted profile can be made from that before surgery using morphing technique.
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