The project is intended to carry out the correlation between occlusal contact and subclinical indicators of physical and mental health. The data collection method combining occlusal detection, physical fitness detection and questionnaire survey is adopted to obtain the target data, and then the machine learning method is used to extract the sensitive technical parameters from the collected data.~1. Data collection~3D occlusal contact detection: It is planned to recruit 1000 healthy volunteers and use oral laser scanner to conduct 3D scanning on them, so as to obtain 3D occlusal digital model data of 1000 healthy volunteers. A control group was set up at the same time, and the model and data of 10000 patients who had previously visited the hospital in our department's existing case database were used for comparative analysis.~Physical fitness test: for the dynamic/static standing balance function test of volunteers, the dynamic/static balance test system developed by our research group is used to test and obtain physical fitness parameters. The sample size is statistically estimated, and the average number of dynamic/static tests is not less than 50. A control group was established at the same time: volunteers with temporomandibular joint disorder and abnormal occlusion.~Psychological evaluation: including volunteers' personality characteristics, mental health level and basic flight ability.~2. The extraction of machine learning occlusal parameters uses the 3D occlusal evaluation system reported by us to calculate the occlusal tightness, and extracts the three-dimensional direction and comprehensive parameters of the upper and lower jaw according to the method reported by us, a total of 10 quantitative indicators.~3. Correlation analysis of occlusal contact and physical and mental health indicators For the collected occlusal parameter data, the physical parameter data obtained from the dynamic/static orthostatic balance function test, and the psychological parameter data obtained from the personality characteristics, mental health level and other tests, the difference of the dynamic/static ability of volunteers with healthy, temporomandibular joint disorder and abnormal occlusion was compared with the analysis of variance; Multiple regression method was used for correlation test; Machine learning is used for cluster analysis.
According to our previous research and clinical observation results, the motor function of the oral, maxillofacial and cervical body is closely related to the occlusal contact. Not only that, the occlusal contact also affects the psychological activities related to movement. There are many technical means to evaluate the occlusion clinically. However, the occlusion is a complex motor organ with more than 3 dimensional (including age factors) morphological characteristics, which makes the occlusion have obvious individualized characteristics. It is of great significance to objectively evaluate oral health to accurately extract occlusal contact features, analyze the relationship between occlusion and the motor function of oral, maxillofacial, neck and body as well as the corresponding psychological characteristics, and establish an evaluation method of occlusal function for evaluating motor function and psychological characteristics.