Deep caries detection using deep learning: from dataset acquisition to detection.

Abstract

Objectives: The study aims to address the global burden of dental caries, a highly prevalent disease affecting billions of individuals, including both children and adults. Recognizing the significant health challenges posed by untreated dental caries, particularly in low- and middle-income countries, our goal is to improve early-stage detection. Though effective, traditional diagnostic methods, such as bitewing radiography, have limitations in detecting early lesions. By leveraging Artificial Intelligence (AI), we aim to enhance the accuracy and efficiency of caries detection, offering a transformative approach to dental diagnostics.

Materials and methods: This study proposes a novel deep learning-based approach for dental caries detection using the latest models, i.e., YOLOv7, YOLOv8, and YOLOv9. Trained on a dataset of over 3,200 images, the models address the shortcomings of existing detection methods and provide an automated solution to improve diagnostic accuracy.

Results: The YOLOv7 model achieved a mean Average Precision (mAP) at 0.5 Intersection over Union (IoU) of 0.721, while YOLOv9 attained a mAP@50 IoU of 0.832. Notably, YOLOv8 outperformed both, with a mAP@0.5 of 0.982. This demonstrates robust detection capabilities across multiple categories, including caries," "Deep Caries," and "Exclusion."

Conclusions: This high level of accuracy and efficiency highlights the potential of integrating AI-driven systems into clinical workflows, improving diagnostic capabilities, reducing healthcare costs, and contributing to better patient outcomes, especially in resource-constrained environments.

Clinical relevance: Integrating these latest YOLO advanced AI models into dental diagnostics could transform the landscape of caries detection. Enhancing early-stage diagnosis accuracy can lead to more precise and cost-effective treatment strategies, with significant implications for improving patient outcomes, particularly in low-resource settings where traditional diagnostic capabilities are often limited.