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

Area of Science:

• Oral and Maxillofacial Surgery
• Biomedical Engineering
• Medical Imaging

Background:

• Severe mandibular and maxillary defects pose challenges for virtual surgical planning due to lack of detailed jaw shape data.
• Accurate reconstruction of jaw anatomy is crucial for successful surgical outcomes in reconstructive dentistry.

Purpose of the Study:

• To develop a method for reconstructing the personalized 3D shape of severely damaged jaws, particularly across the midline.
• To address the data limitations in virtual surgical planning for complex mandibular and maxillary defects.

Main Methods:

• Utilized two linear regression methods (Method I and Method II) to reconstruct key points of damaged jaws based on remaining anatomy.
• Employed computed tomographic data from 44 normal adult East Chinese subjects.
• Analyzed position errors and probability distributions to evaluate reconstruction accuracy.

Main Results:

• Method I, employing 2D regression, demonstrated the best overall performance, reducing position errors to below 5 mm for most key points.
• Method II yielded similar results to Method I but exhibited cumulative errors.
• Collected CT data from 44 subjects, extracting 16 key points per jaw.

Conclusions:

• Linear regression is a viable technique for accurately locating key points in jaw reconstruction.
• Two-dimensional regression offers the most effective approach for repairing jaw defects.
• The developed method serves as a valuable reference for surgical planning and execution in complex cases.