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Evaluation of the accuracy of fully automatic cephalometric analysis software with artificial intelligence algorithm.

Gökhan Serhat Duran¹, Şule Gökmen², Kübra Gülnur Topsakal¹

¹Department of Orthodontics, Gulhane Faculty of Dental Medicine, University of Health Sciences, Ankara, Turkey.

Orthodontics & Craniofacial Research

|January 17, 2023

Summary

Artificial intelligence (AI) in cephalometric analysis shows good consistency for angular measurements but weaker results for linear and soft tissue parameters. Manual observer input is recommended to improve accuracy in AI-based cephalometric software.

Keywords:

artificial intelligence automatic landmark detection cephalometric analysis

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Area of Science:

Dentistry
Medical Imaging
Artificial Intelligence

Background:

Cephalometric analysis is crucial for orthodontic diagnosis and research.
Traditional methods rely on manual landmark identification, which can be time-consuming and prone to variability.
The integration of artificial intelligence (AI) offers potential for automated and efficient cephalometric analysis.

Purpose of the Study:

To compare the accuracy of fully automatic AI-powered cephalometric analysis software with traditional non-automated software.
To evaluate the suitability of AI software for clinical diagnosis and research applications.

Main Methods:

A retrospective study utilizing lateral cephalometric radiographs from individuals aged 12-20 years.
Manual cephalometric measurements were performed using Dolphin software (non-automated).
The same radiographs were analyzed using two AI-powered software: OrthoDx™ and WebCeph, for fully automatic landmark identification and measurements.

Main Results:

High consistency (ICC > 0.75) was observed between AI software (OrthoDx™, WebCeph) and Dolphin for angular measurements.
Lower consistency (ICC < 0.50) was found for linear measurements and soft tissue parameters between the software.
Statistically significant differences from zero were noted in linear and soft tissue measurements, indicating discrepancies.

Conclusions:

Fully automatic AI cephalometric analysis software demonstrates comparable accuracy to non-automated software for angular measurements.
Discrepancies in linear and soft tissue measurements suggest limitations in current AI algorithms.
Observer manual intervention is necessary to enhance the precision of AI-based cephalometric analysis and minimize errors.