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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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Correction: Kuc et al. Tension-Dominant Orthodontic Loading and Buccal Periodontal Phenotype Preservation: An Integrative Mechanobiological Model Supported by FEM and a Proof-of-Concept CBCT. <i>J. Funct. Biomater.</i> 2026, <i>17</i>, 47.

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Related Experiment Video

Updated: Jul 16, 2026

Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
10:23

Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

Automated YOLO-Based Cephalometric Landmark Detection for ANB-Based Skeletal Classification: A Retrospective

Jacek Kotula1, Marcin Konarzewski2, Jakub Polkowski2

  • 1Department of Dentofacial Orthopedics and Orthodontics, Wroclaw Medical University, Krakowska 26, 50-425 Wroclaw, Poland.

Journal of Clinical Medicine
|July 15, 2026
PubMed
Summary

This study shows that YOLO-based AI accurately detects cephalometric landmarks, achieving high agreement with expert skeletal classifications. Bounding-box size is crucial for AI accuracy in orthodontic diagnosis.

Keywords:
ANB angleYOLOartificial intelligencecephalometryclinical validationconvolutional neural networkdeep learningdiagnostic agreementlandmark detectionorthodonticsreproducibilityskeletal classification

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Last Updated: Jul 16, 2026

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Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Area of Science:

  • Orthodontics
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Deep learning for automated cephalometric landmark detection can enhance orthodontic diagnosis.
  • AI accuracy's clinical relevance hinges on error propagation in measurements and classifications.
  • Evaluating YOLO-based models for ANB-based skeletal classification agreement with expert diagnoses.

Purpose of the Study:

  • To evaluate YOLO-based AI model configurations for cephalometric landmark detection.
  • To quantify agreement between AI-derived and expert-derived ANB-based skeletal classifications.
  • To assess the impact of model architecture, bounding-box size, dataset scale, and training epochs on accuracy.

Main Methods:

  • Trained 12 YOLO-based models on lateral cephalograms, evaluating on an independent test set.
  • Focused on Sella, Nasion, A-point, and B-point landmarks.
  • Assessed localization accuracy (MRE, SDR) and downstream ANB classification concordance using statistical measures like Cohen's κ.

Main Results:

  • The best model achieved 87.2% SDR@4 mm and 3.10±1.00 mm MRE.
  • ANB-based skeletal classification showed 96.9% concordance with expert assessments (κ = 0.946).
  • Bounding-box size significantly impacted localization accuracy, with smaller boxes yielding better results.

Conclusions:

  • YOLO-based AI demonstrates promising diagnostic concordance for ANB-based skeletal classification.
  • Prospective, multi-center validation is needed before clinical deployment.
  • A confidence-aware workflow and careful bounding-box calibration are recommended for AI implementation.