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Related Concept Videos

Classification of Bones01:18

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The long...

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Updated: Jul 15, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

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Published on: November 28, 2025

Artificial intelligence for comprehensive skeletal maturity assessment: a unified CBCT-based deep learning framework.

Amanda Nikho1, Lauren Mills1, Omid Halimi Milani2

  • 1Department of Orthodontics, College of Dentistry, University of Illinois Chicago, Chicago, IL, USA.

Scientific Reports
|July 13, 2026
PubMed
Summary

A new deep learning framework unifies skeletal maturity assessment using cervical vertebral maturity (CVM), spheno-occipital synchondrosis (SOS), and mid-palatal suture (MPS) markers. The two-phase approach achieved 78.13% accuracy, offering a comprehensive skeletal overview.

Keywords:
Cone beam computed tomographyDeep learningKnowledge distillationMid-palatal suture (MPS)SegmentationSpheno-occipital synchondrosis (SOS)The cervical vertebral maturity (CVM)

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Area of Science:

  • Dentistry and Orthodontics
  • Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Skeletal maturity assessment is vital in orthodontics and craniofacial surgery.
  • Current methods using cervical vertebral maturity (CVM), spheno-occipital synchondrosis (SOS), and mid-palatal suture (MPS) are subjective or site-specific.
  • Existing automated tools offer partial evaluations and fail with missing regions.

Purpose of the Study:

  • To develop a unified deep learning framework for automated skeletal maturity assessment.
  • To integrate CVM, SOS, and MPS evaluation into a single system.
  • To overcome limitations of existing site-specific and manual staging methods.

Main Methods:

  • A deep learning framework was developed to assess skeletal maturity using CVM, SOS, and MPS markers from CBCT scans.
  • Two configurations were tested: a single-phase classifier and a hierarchical two-phase classifier with confidence-aware routing.
  • The dataset comprised 364 CVM, 723 SOS, and 618 MPS slices, with expert-assigned maturation stages.

Main Results:

  • The single-phase classifier achieved 64.17% accuracy.
  • The hierarchical two-phase classifier achieved 77.94% accuracy.
  • Incorporating confidence-aware routing further improved accuracy to 78.13% in the two-phase model.

Conclusions:

  • The unified two-phase deep learning approach demonstrates superior performance for skeletal maturity assessment.
  • This framework provides a comprehensive skeletal overview by integrating multiple biological markers.
  • The automated tool serves as an adjunct to, not a replacement for, human expertise in clinical practice.