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Related Experiment Video

Updated: May 14, 2026

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

An Automatic Grading System for Orthodontically Induced External Root Resorption Based on Deep Convolutional Neural

Shuxi Xu^1,2,3, Houli Peng^1,2,3, Lanxin Yang^1,2,3

¹College of Stomatology, Chongqing Medical University, Chongqing, 401147, China.

Journal of Imaging Informatics in Medicine

|February 23, 2024

Summary

This summary is machine-generated.

Deep learning models accurately grade tooth root resorption after orthodontic treatment. An automated system using EfficientNet-B4 achieved 94% accuracy, outperforming human orthodontists and aiding clinical diagnosis.

Keywords:

Artificial intelligence Convolutional neural network Diagnostic system Orthodontics Root resorption

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Published on: February 23, 2024

Area of Science:

Dentistry
Medical Imaging
Artificial Intelligence

Background:

Orthodontically induced external root resorption (OIERR) is a frequent complication of orthodontic therapy.
Accurate grading of OIERR is essential for timely clinical management and treatment adjustments.

Purpose of the Study:

To develop and evaluate deep convolutional neural networks (CNNs) for automated OIERR grading.
To construct a reliable AI-based system for OIERR assessment in clinical practice.

Main Methods:

Six pre-trained CNNs (EfficientNet variants and MobileNet-V3) were trained and validated on 2146 tooth slices.
Performance was assessed using cross-validation and compared against orthodontist evaluations.
Grad-CAM was employed to visualize model decision-making areas.

Main Results:

All six CNN models demonstrated high performance in OIERR grading, with a mean accuracy of 0.92.
The developed AI system surpassed orthodontists' mean accuracy (0.82).
EfficientNet-B4 with fivefold cross-validation achieved the highest accuracy (0.94), identifying the apical region as critical.

Conclusions:

CNNs show excellent capability for OIERR grading, outperforming human experts.
The proposed AI system offers a promising, reliable diagnostic support tool for orthodontists.
Automated OIERR grading can enhance clinical decision-making and patient care.