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Background <p>Children in the mixed dentition stage are susceptible to dental caries, necessitating frequent examination and preventive measures. In this study, convolutional neural networks (CNNs) were used to develop a model for detecting oral health status in mixed dentition.</p> Methods <p>Intraoral images of dentition spanning 2467 occlusal surfaces were collected from 7–9-year-old children for training and testing. The health status of all teeth, including caries, fillings, intraoral devices, pigments, and black stains, was tested. For the first permanent molar, indicator testing was conducted to determine the eruption conditions, fissure morphology, and sealing conditions. ResNet50 was trained for segmentation tasks, and TResNet was trained for detection tasks. The statistical analysis included calculations of the F1 score (F1) and area under the receiver operating characteristic (ROC) curve (AUC).</p> Results <p>The model was able to correctly detect caries (F1, 0.777; AUC, 0.964), fillings (F1, 0.746; AUC, 0.893), pigmentation (F1, 0.836; AUC, 0.870), intraoral devices (F1, 0.800; AUC, 1.00) and black stains (F1, 0.867; AUC, 0.988). The model demonstrated good capabilities for detecting deep fissures (F1, 0.927; AUC, 0.951), completely erupted teeth (F1, 0.882; AUC, 0.944), and sealed teeth (F1, 0.947; AUC, 0.986), but it demonstrated a weak ability to detect incompletely erupted teeth (F1, 0.727; AUC, 0.871) and shallow fissures (F1, 0.632; AUC, 0.934).</p> Conclusions <p>The performance of the model was verified in detecting tooth status and screening teeth suitable for fissure sealants, but the scale of rare labels was also inadequate and requires optimization.</p>

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Automatic recognition of oral health status in mixed dentition via intraoral photography

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月10日

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  • Zhuoying Liu,
  • Shen Zhao,
  • Yikang Wang,
  • Yina Cao,
  • Huancai Lin,
  • Liangyue Pang

摘要

Background

Children in the mixed dentition stage are susceptible to dental caries, necessitating frequent examination and preventive measures. In this study, convolutional neural networks (CNNs) were used to develop a model for detecting oral health status in mixed dentition.

Methods

Intraoral images of dentition spanning 2467 occlusal surfaces were collected from 7–9-year-old children for training and testing. The health status of all teeth, including caries, fillings, intraoral devices, pigments, and black stains, was tested. For the first permanent molar, indicator testing was conducted to determine the eruption conditions, fissure morphology, and sealing conditions. ResNet50 was trained for segmentation tasks, and TResNet was trained for detection tasks. The statistical analysis included calculations of the F1 score (F1) and area under the receiver operating characteristic (ROC) curve (AUC).

Results

The model was able to correctly detect caries (F1, 0.777; AUC, 0.964), fillings (F1, 0.746; AUC, 0.893), pigmentation (F1, 0.836; AUC, 0.870), intraoral devices (F1, 0.800; AUC, 1.00) and black stains (F1, 0.867; AUC, 0.988). The model demonstrated good capabilities for detecting deep fissures (F1, 0.927; AUC, 0.951), completely erupted teeth (F1, 0.882; AUC, 0.944), and sealed teeth (F1, 0.947; AUC, 0.986), but it demonstrated a weak ability to detect incompletely erupted teeth (F1, 0.727; AUC, 0.871) and shallow fissures (F1, 0.632; AUC, 0.934).

Conclusions

The performance of the model was verified in detecting tooth status and screening teeth suitable for fissure sealants, but the scale of rare labels was also inadequate and requires optimization.