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Related Experiment Video

Updated: May 30, 2025

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

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Enhancing furcation involvement classification on panoramic radiographs with vision transformers.

Xuan Zhang¹, Enting Guo², Xu Liu³

¹Department of Periodontics, Affiliated Hospital of Medical School, Nanjing Stomatological Hospital, Research Institute of Stomatology, Nanjing University, Nanjing, China. zhxuan2015@163.com.

BMC Oral Health

|January 29, 2025

Summary

The Vision Transformer (ViT) model accurately classifies furcation involvement (FI) in molars using panoramic radiographs. This deep learning approach surpasses traditional models, offering a potentially lower-cost, lower-radiation alternative for dental diagnostics.

Keywords:

Deep learning Furcation involvement Panoramic radiograph Vision transformer

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Published on: May 24, 2022

Area of Science:

Dentistry
Artificial Intelligence
Medical Imaging

Background:

Furcation involvement (FI) impacts tooth prognosis and treatment, but its assessment is complex due to anatomical variations.
Cone-beam computed tomography (CBCT) improves diagnostic accuracy for FI but is limited by cost and radiation.
Developing accessible diagnostic tools for FI is crucial for effective dental treatment planning.

Purpose of the Study:

To evaluate the performance of the Vision Transformer (ViT) model for classifying furcation involvement (FI) on panoramic radiographs.
To compare the ViT model's efficacy against traditional deep learning (DL) models for FI detection.
To explore advanced AI approaches for improving the diagnosis of dental conditions using standard radiographic imaging.

Main Methods:

A dataset of 1,568 tooth images from 506 panoramic radiographs was utilized.
A Vision Transformer (ViT) model was developed and assessed for classifying FI.
The ViT model's performance was compared with Multi-Layer Perceptron (MLP), VGGNet, and GoogLeNet models.

Main Results:

The ViT model achieved superior performance with the highest precision (0.98), recall (0.92), F1 score (0.95), and accuracy (92%).
ViT demonstrated the highest area under the curve (AUC) at 98%, significantly outperforming other models (p < 0.05).
Gradient-weighted class activation mapping (Grad-CAM) analysis confirmed ViT's focus on relevant image areas for prediction.

Conclusions:

Deep learning algorithms, particularly ViT, can effectively classify furcation involvement from panoramic radiographs.
The ViT model shows significant potential to advance dental image classification beyond traditional DL methods.
This AI-driven approach may reduce patient radiation exposure and costs while enhancing diagnostic precision for furcation involvement.