# An Integrated System for Detecting and Numbering Permanent and Deciduous Teeth Across Multiple Types of Dental X-Ray Images Based on YOLOv8

## TL;DR

This paper introduces a system using YOLOv8 to detect and number both permanent and deciduous teeth in various dental X-ray images, improving diagnostic accuracy in dental care.

## Contribution

The novelty lies in integrating multiple dental X-ray types with a novel preprocessing method for accurate tooth detection and numbering in both adults and children.

## Key findings

- The system achieved 98.16% detection precision for teeth with image enhancement, a 3% improvement over non-enhanced models.
- Tooth numbering accuracy was 98.5% for permanent and 96.3% for deciduous teeth, surpassing existing methods by 5.6%.
- The proposed method shows practical value in diagnosing tooth loss and identifying missing teeth for improved dental treatment.

## Abstract

Background/Objectives: In dental medicine, the integration of various types of X-ray images, such as periapical (PA), bitewing (BW), and panoramic (PANO) radiographs, is crucial for comprehensive oral health assessment. These complementary imaging modalities provide diverse diagnostic perspectives and support the early detection of oral diseases, thereby enhancing treatment outcomes. However, there is currently no existing system that integrates multiple types of dental X-rays for both adults and children to perform tooth localization and numbering. Methods: Therefore, this study aimed to propose a system based on YOLOv8 that integrates multiple dental X-ray images and automatically detects and numbers both permanent and deciduous teeth. Through image preprocessing, various types of dental X-ray images were standardized and enhanced to improve the recognition accuracy of individual teeth. Results: With the implementation of a novel image preprocessing method, the system achieved a detection precision of 98.16% for permanent and deciduous teeth, representing a 3% improvement over models without image enhancement. In addition, the system attained an average tooth numbering accuracy of 98.5% for permanent teeth and 96.3% for deciduous teeth, surpassing existing methods by 5.6%. Conclusions: These results might highlight the innovation of the proposed image processing method and show its practical value in assisting clinicians with accurate diagnosis of tooth loss and the identification of missing teeth, ultimately contributing to improved diagnosis and treatment in dental care.