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Three dimensional maxillary growth modeling in newborns.

R Bruggink1,2, F Baan3,4, G J C Kramer5

  • 1Department of Dentistry, section of Orthodontics and Craniofacial Biology, Radboud University Medical Center, Philips van Leydenlaan 25, 6525 EX, Nijmegen, The Netherlands. orthodontie@radboudumc.nl.

Clinical Oral Investigations
|January 13, 2019
PubMed
Summary
This summary is machine-generated.

This study developed a semi-automatic segmentation tool for accurate maxillary and palatal growth analysis in newborns. The method provides reliable data for clinical evaluation and future growth studies.

Keywords:
Dental modelsDiagnostic imagingImaging, three-dimensionalMaxillofacial developmentOrthodonticsPalate

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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.