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Related Experiment Video

Updated: Oct 11, 2025

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

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

Published on: September 8, 2023

Assessment of automatic cephalometric landmark identification using artificial intelligence.

Galina Bulatova¹, Budi Kusnoto¹, Viana Grace¹

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

Orthodontics & Craniofacial Research

|November 29, 2021

Summary

This summary is machine-generated.

Artificial intelligence (AI) using deep learning convolutional neural networks (CNNs) demonstrated comparable accuracy to manual tracing for cephalometric landmark identification. This AI approach may enhance efficiency in orthodontic practice and research without sacrificing precision.

Keywords:

artificial intelligence automated cephalometry landmark identification

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Artificial Intelligence Approaches to Assessing Primary Cilia

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

Area of Science:

Orthodontics and Dentofacial Orthopedics
Medical Imaging Analysis
Artificial Intelligence in Healthcare

Background:

Cephalometric analysis is crucial for orthodontic diagnosis and treatment planning.
Manual tracing of cephalometric landmarks is time-consuming and subject to inter-observer variability.
Deep learning algorithms offer potential for automated and accurate landmark identification.

Purpose of the Study:

To compare the accuracy of cephalometric landmark identification between an artificial intelligence (AI) deep learning convolutional neural network (CNN) algorithm (You Only Look Once, Version 3 - YOLOv3) and manual tracing (MT).
To evaluate the efficiency and precision of AI-driven cephalometric analysis.

Main Methods:

110 cephalometric images from the American Association of Orthodontists Foundation (AAOF) Legacy Denver collection were analyzed.
Lateral cephalograms were manually traced by a senior orthodontic resident using Dolphin Imaging software.
The same images were processed by AI software (Ceppro DDH Inc.), extracting coordinates for 16 cephalometric points relative to Sella; differences were assessed against a 2mm threshold using paired t-tests (P < .05).

Main Results:

No statistically significant difference in absolute differences was found between manual tracing (MT) and artificial intelligence (AI) groups for 12 out of 16 cephalometric points.
The study indicates high concordance between AI and manual landmark identification.

Conclusions:

Artificial intelligence (AI) shows potential to increase efficiency in cephalometric tracings.
AI-based cephalometric analysis can be performed without compromising accuracy in routine clinical practice and research settings.
The YOLOv3 algorithm provides a reliable alternative for cephalometric landmark identification.