# Transformer-based intelligent detection model for early dental caries in panoramic radiographs

## TL;DR

This paper introduces a Transformer-based model for detecting early dental caries in panoramic radiographs, outperforming traditional methods and dentists in accuracy and speed.

## Contribution

A novel Transformer-based model with enhanced feature fusion and attention mechanisms for early dental caries detection.

## Key findings

- The model achieved 87.3% mean average precision across all caries stages.
- It showed 81.3% sensitivity for D1 lesions and 84.7% for D2 lesions.
- The system processes images in real-time (70 milliseconds per radiograph).

## Abstract

Early detection of dental caries in panoramic radiographs remains challenging due to subtle radiographic features and complex anatomical structures. This study develops a Transformer-based intelligent detection model specifically optimized for identifying early-stage carious lesions in panoramic dental images. The proposed architecture integrates enhanced multi-scale feature fusion mechanisms, spatially-aware attention optimization, and improved two-dimensional positional encoding to capture global contextual relationships while maintaining fine-grained feature discrimination. A comprehensive dataset comprising 3,856 panoramic radiographs with 12,847 annotated carious lesions across severity grades (D1-D4) was constructed for model development and validation. The model achieved 87.3% mean average precision (mAP) across all caries stages, with notable sensitivity of 81.3% for D1 lesions and 84.7% for D2 lesions, surpassing conventional CNN-based approaches and average dentist performance. The system processes images in real-time (70 milliseconds per radiograph). This research demonstrates the efficacy of domain-adapted Transformer architectures for early dental caries detection and establishes its potential utility as a decision support tool for enhancing diagnostic accuracy and screening efficiency in dental practice.