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A family-centered orthodontic screening approach using a machine learning-based mobile application.

Banu Kılıç¹, Ahmed Hassan İbrahim², Selahattin Aksoy²

¹Bezmialem Vakif University, Istanbul, Turkey.

Journal of Dental Sciences

|February 2, 2024

Summary

A new mobile app uses AI to detect skeletal malocclusion from a single photo, aiding early orthodontic diagnosis in children. This tool empowers parents with information for timely intervention.

Keywords:

Angle Class III Artificial intelligence Early diagnosis Machine learning Malocclusion Telemedicine

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Using an Automated Hirschberg Test App to Evaluate Ocular Alignment

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Published on: March 24, 2020

Area of Science:

Orthodontics
Artificial Intelligence
Pediatric Dentistry

Background:

Skeletal orthodontic deformities impact function and aesthetics, necessitating early detection.
Delayed orthodontic checkups often lead to missed ideal treatment windows for children.
Mobile technology offers a novel approach for accessible preliminary orthodontic screening.

Purpose of the Study:

To develop and evaluate a machine learning-based mobile application for early skeletal malocclusion diagnosis.
To assist parents in identifying potential orthodontic issues before the optimal treatment age.
To improve access to orthodontic care through user-friendly technology.

Main Methods:

A retrospective study involving 524 pre-pubertal children (ages 5-12) was conducted.
A mobile application was developed to analyze facial photographs for skeletal malocclusion indicators.
Machine learning models were trained and validated to classify different types of malocclusion.

Main Results:

The Class III malocclusion detection model achieved over 81% accuracy.
A separate validation dataset showed 69% accuracy for Class II vs. Class I malocclusion detection.
The application successfully identifies skeletal malocclusion from single photographs.

Conclusions:

The mobile application provides preliminary diagnoses, empowering parents with crucial information.
Early detection and informed decision-making can improve orthodontic treatment outcomes.
This technology has the potential to enhance orthodontic care accessibility, particularly in underserved areas.