Restoring Skeletal Marker Points for Severe Maxillary and Mandibular Jaw Defects Using a Linear Regression Approach

Abstract

For severe mandibular or maxillary defects across the midline, doctors often lack data on the shape of the jaws when designing virtual surgery. This study sought to repair the personalized 3-dimensional shape of the jaw, particularly when the jaw is severely damaged. Two linear regression methods, denoted method I and method II, were used to reconstruct key points of the severely damaged maxilla or mandible based on the remaining jaw. The predictor variable was the position of key points. Outcome variables were the position of key points and the error between the predicted and actual positions. Another variable was the average error. In the final data analysis, the effect of the method was judged based on the mean error and error probability distribution. Computed tomographic data of jaws from 44 normal adults in East China were collected over 2years by the Shanghai Jiao Tong University School of Medicine (Shanghai, China). Sixteen 16 key points were extracted for each jaw. Method I showed that 2-dimensional regression can yield the best overall result and that the position error of most points can be decreased to smaller than 5mm. The result of method II was similar to that of method I but showed cumulative errors. Linear regression can be used to locate key points. Two-dimensional regression has the best effect, which can be used as a reference to develop a surgical plan and perform surgery.