Geometry-Based Algorithm for the Prediction of Nonpathologic Mandibular Movement

Lemoine, Jeremy J.; Xia, James J.; Andersen, Clark R.; Gateno, Jaime; Buford, William; Liebschner, Michael A.K.

doi:10.1016/j.joms.2007.05.011

« Previous Next »Journal of Oral and Maxillofacial Surgery
Volume 65, Issue 12 , Pages 2411-2417, December 2007

Geometry-Based Algorithm for the Prediction of Nonpathologic Mandibular Movement

Jeremy J. Lemoine, PhD
- Former Graduate Student, Department of Bioengineering, Rice University, Houston, TX.
James J. Xia, MD, PhD
- Director, Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, The Methodist Hospital Research Institute; and Assistant Professor, Division of Pediatric Plastic Surgery, Department of Surgery, Medical School, University of Texas Health Science Center, Houston, TX.
Clark R. Andersen, BS
- Computer Programmer, Orthopaedic Biomechanics Laboratory, University of Texas Medical Branch at Galveston, Galveston, TX.
Jaime Gateno, DDS, MD
- Chairman, Department of Oral and Maxillofacial Surgery, The Methodist Hospital Research Institute, Houston, TX; Professor of Clinical Surgery, Weil Medical College of Cornell University, New York, NY; and Associate Professor, Division of Pediatric Plastic Surgery, Department of Surgery, Medical School, University of Texas Health Science Center, Houston, TX.
William Buford Jr, PhD
- Associate Professor, Department of Orthopaedics and Rehabilitation, University of Texas Medical Branch at Galveston, Galveston, TX.
Michael A.K. Liebschner, PhD
- Assistant Professor, Department of Bioengineering, Rice University, Houston, TX.
- Address correspondence and reprint requests to Dr Liebschner: Department of Bioengineering-MS142, PO Box 1892, Houston, TX 77251-1892

Purpose

The goal of this study was to create a model for human mandibular movement prediction based on the geometry of the mandible.

Materials and Methods

Ten nonpathologic individuals underwent motion tracking and sagittal radiographs. From the data, a mathematical algorithm for mandibular movement prediction was developed based on mandibular geometry. The algorithm was then used to predict the mandibular movement of a cadaver subject. The algorithm was also validated in a living subject by comparing to recorded mandibular movement.

Results

Both mandibular movement predictions were free of bone collisions and showed mandibular movement that mimicked the in vivo situation. Comparisons between the predicted and recorded mandibular movements for the living human subject verified that the prediction model was accurate.

Conclusions

The mandibular movement can be predicted based on the mandibular opening radius. The model is validated in living human subjects and shows effectiveness in predictions for cadaver models. Mandibular movement prediction may be a useful tool for physicians as well as investigators who focus on temporomandibular joint research.