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Published on: February 2, 2020
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Deep Learning in Oral Hygiene: Automated Dental Plaque Detection via YOLO Frameworks and Quantification Using the
Alfonso Ramírez-Pedraza1,2, Sebastián Salazar-Colores3, Crystel Cardenas-Valle4
1Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, Querétaro 76090, Mexico.
Diagnostics (Basel, Switzerland)
|January 25, 2025
Summary
Advanced AI models can now detect different stages of dental plaque, improving early oral disease intervention. This automated approach shows promise for better oral health, especially in underserved areas.
Area of Science:
- Oral Health
- Artificial Intelligence
- Medical Imaging
Background:
- Oral diseases like caries and periodontitis are globally prevalent, often stemming from dental plaque.
- Early detection of plaque stages is crucial for timely intervention and disease prevention.
- Current methods for plaque assessment often rely on manual visual inspection, which can be subjective and time-consuming.
Purpose of the Study:
- To evaluate the efficacy of state-of-the-art YOLO (You Only Look Once) object detection architectures for identifying three distinct stages of dental plaque: new, mature, and over-mature.
- To compare the performance of different YOLOv9, YOLOv10, and YOLOv11 variants in plaque detection under varied imaging conditions.
- To assess the potential of automated plaque detection to enhance early diagnosis and reduce the burden of oral diseases.
Main Methods:
- A dataset of 531 RGB images from 177 individuals was collected using mobile devices after plaque disclosure.
- Images underwent preprocessing for lighting and color normalization.
- YOLOv9, YOLOv10, and YOLOv11 models were trained to detect and classify plaque stages, with performance metrics including precision, recall, and mAP@50.
Main Results:
- YOLOv11m achieved the highest mean Average Precision at 50% IoU (mAP@50) of 0.713, demonstrating superior performance in detecting over-mature plaque.
- Older plaque stages were generally detected more accurately than newer plaque, which can be challenging due to its similarity to gingival tissue.
- Analysis using the O'Leary index revealed that over half of the study participants had severe plaque levels.
Conclusions:
- Advanced YOLO models are feasible for automated dental plaque detection across diverse imaging scenarios.
- This AI-driven approach has the potential to streamline clinical workflows and improve the accuracy of early oral disease diagnosis.
- Automated plaque detection can significantly contribute to mitigating oral health issues, particularly in resource-limited settings.
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