Tooth Detection and Segmentation on Occlusal Surfaces in Early Mixed Dentition Using Deep Learning
DOI:
https://doi.org/10.5753/jdhbi.2026.7916Keywords:
Inteligencia artificial aprendizaje profundo, redes neuronales convolucionales, dentición mixta, Inteligencia Artificial, Aprendizaje Profundo, Redes Neuronale Convolucionales, Dentición MixtaAbstract
Artificial intelligence, particularly Convolutional Neural Networks (CNNs), has shown great potential for the detection of oral health conditions, including dental caries. Objectives: To evaluate the performance of CNNs in the detection and segmentation of posterior teeth during the early phase of mixed dentition. Materials and Methods: A total of 945 images of posterior teeth were collected in a school-based setting. The dataset was divided into training and testing subsets. Three CNN architectures — YOLOv11, U-Net, and DeepLabv3 — were compared based on precision, recall, and their ability to detect partially erupted teeth. Results: YOLOv11 achieved a precision of 0.967 and a recall of 0.938, successfully identifying 96.1% of partially erupted permanent teeth. U-Net demonstrated a precision of 0.953 and a recall of 0.951, detecting 78.4% of partially erupted teeth, while DeepLabv3 achieved a precision of 0.963 and a recall of 0.944, detecting 76.5% of these teeth. Conclusions: All three CNNs demonstrated high accuracy in detecting and segmenting posterior teeth in children. However, YOLOv11 outperformed both U-Net and DeepLabv3 in detecting partially erupted teeth, highlighting its potential for use in studies involving early mixed dentition. Clinical Relevance: Identifying the most suitable CNN architecture for detecting caries in mixed dentition may help standardize diagnosis and reduce clinical time.
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Derechos de autor 2026 Bruna Cristine Dias, Mateus Felipe de Cássio Ferreira, Luan Matheus Trindade Dalmazo, Luciana Reichert da Silva Assunção, Lucas Ferrari de Oliveira
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
