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Artificial Intelligence Methods in Cephalometric Image Analysis-A Systematic Narrative Review.

Katarzyna Zaborowicz¹, Maciej Zaborowicz², Katarzyna Cieślińska¹

¹Department of Orthodontics and Facial Malformations, Poznan University of Medical Sciences, Bukowska 70, 60-812 Poznań, Poland.

Journal of Clinical Medicine

|March 14, 2026

Summary

This summary is machine-generated.

Artificial intelligence (AI) in cephalometric analysis offers clinically acceptable accuracy for orthodontic diagnostics. AI tools enhance repeatability and reduce analysis time, supporting clinical decisions for craniofacial growth and treatment planning.

Keywords:

artificial intelligence cephalometric analysis cervical vertebral maturation (CVM)convolutional neural network (CNN)deep learning landmark detection machine learning neural networks orthodontics orthognathic surgery

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Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model

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Published on: November 4, 2025

Area of Science:

Orthodontics
Medical Imaging
Artificial Intelligence

Background:

Information technology advancements, including AI, are crucial for orthodontic diagnostics.
Cephalometric images are vital for orthodontic treatment planning, providing data on craniofacial growth.
Machine learning and automation are increasingly applied to analyze these images.

Purpose of the Study:

To review current AI applications in cephalometric image analysis.
Focus on studies published between 2020-2025 from Scopus and Web of Science databases.

Main Methods:

Systematic review of 20 key studies.
Analysis of AI models including CNN, YOLO, BCNN, ANN, and others.
Evaluation of performance in landmark detection and classification tasks.

Main Results:

AI landmark detection errors are 1-2 mm, within clinical limits.
CNN and YOLO models match orthodontist accuracy; BCNN provides uncertainty estimates.
ANN models achieve 95% accuracy for cervical vertebral maturity (CVM); screening tools reach 96.3% agreement.

Conclusions:

AI tools demonstrate clinically acceptable accuracy in cephalometric analysis.
AI improves repeatability and reduces analysis time in semi-automated workflows.
AI shows potential as a reliable tool for CVM and surgical eligibility assessment, pending broader validation.