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Related Experiment Video

Updated: Jun 13, 2025

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
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Cone-beam CT landmark detection for measuring basal bone width: a retrospective validation study.

Juan Dai1, Xinge Guo2,3, Hongyuan Zhang2,3

  • 1Department of Stomatology, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Xueyuan Avenue, Nanshan District, Shenzhen, Guangdong, 518055, China.

BMC Oral Health
|September 14, 2024
PubMed
Summary

A new deep learning (DL) model accurately measures basal bone width from cone-beam computed tomography (CBCT) images, aiding in diagnosing maxillary transverse deficiency and improving orthodontic treatment planning.

Keywords:
Basal bone widthCBCTDeep learningMaxillary transverse deficiency

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Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate basal bone width assessment is crucial for diagnosing maxillary transverse deficiency.
  • Distinguishing normal occlusion from deficiency aids in treatment planning, including maxillary expansion.

Purpose of the Study:

  • To evaluate a deep learning (DL) model's effectiveness in measuring basal bone width.
  • To assess the consistency of automated DL measurements against manual measurements.

Main Methods:

  • A U-Net based DL model was developed and trained on 80 cone-beam computed tomography (CBCT) images.
  • The model was validated on 10 scans and tested on 34, with measurements compared to manual ones using concordance correlation coefficient (CCC).

Main Results:

  • The DL model measured basal bone width in approximately 1.5 seconds per CBCT image.
  • High concordance was found between DL and manual measurements (CCCs of 0.96 for maxillary and 0.98 for mandibular width).
  • Successful detection rates ranged from 71.34% to 91.18% for different measurement ranges.

Conclusions:

  • The developed DL framework significantly improved diagnostic accuracy for maxillary width assessment.
  • This DL model shows potential as a valuable computer-aided diagnostic tool in orthodontics.