Tooth decay is a dental condition characterized by the deterioration of tooth tissue, originating from the outer surface and progressing to the pulp. Severe tooth decay, evolving into cavities, necessitates timely intervention to avert more serious dental health issues. Common treatment procedures include filling and extraction of affected teeth. Presently, dentists conduct examinations for tooth decay by manually tallying affected, missing, and filled teeth using an odontogram—a human tooth code diagram. This data is then recorded in patients' dental medical records. Recognizing the need for automation in assessing patients' experiences of tooth decay, this research endeavors to develop a model capable of detecting decayed, missing, and filled teeth using variations of the YOLOv5 and YOLOv8 model architectures. The results of the training evaluation demonstrate the efficacy of YOLOv5l with a learning rate of 10-2, exhibiting a high precision value of 0.97, recall of 0.858, and a mean average precision (mAP) of 0.904 within 1 hour and 18 minutes. According to the curves obtained in training process, YOLOv5l shows great performance on the dental caries dataset, but precautions like early stopping are needed for a reliable and generalizable model. In contrast, YOLOv8, offers better training stability and larger variants perform better on the dental caries dataset, improving detection capabilities with continued training epochs.
Key words: Caries Detection, Detection Model, Deep Learning, DMF-T, Tooth Decay
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