Three dimensional maxillary growth modeling in newborns

Area of Science:

• Craniofacial development
• Pediatric dentistry
• Medical imaging analysis

Background:

• Accurate assessment of infant maxillary arch and palatal growth is crucial for early diagnosis and intervention.
• Existing methods may lack precision or be time-consuming.
• Developing reliable growth profiles aids in understanding normal development and identifying anomalies.

Purpose of the Study:

• To create an accurate, intuitive, semi-automatic segmentation technique.
• To calculate average maxillary arch and palatal growth profiles in healthy newborns.
• To establish growth models for the first year of life.

Main Methods:

• Digitalization of five dental impressions from 70 newborns within their first year.
• Development and application of a semi-automatic segmentation tool for maxillary dimension assessment.
• Utilizing random effect models to describe growth and simulate a population of 10,000 newborns.
• Validation of segmentation through inter- and intra-observer variability testing.

Main Results:

• High inter-observer agreement (Pearson correlation coefficients 0.94–1.00) and repeatability (0.87–1.00).
• Minor, significant differences in landmark placement between observers for tuberosity distance.
• Successful generation of second or third-degree growth curves for all measured parameters.
• Demonstrated accuracy and reliability of the developed segmentation technique.

Conclusions:

• The developed semi-automatic segmentation method enables objective clinical evaluation of maxillary growth in infants.
• The resulting growth models are valuable for research on healthy newborns and for analyzing growth and treatment outcomes in children with craniofacial anomalies.