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A deep-learning system for diagnosing ectopic eruption.

Haojie Yu1, Zheng Cao2, Gaozhi Pang3

  • 1Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou, Zhejiang, China.

Journal of Dentistry
|October 18, 2024
PubMed
Summary

This study developed an AI deep-learning model to predict ectopic tooth eruption in children. The model accurately segments teeth and aids in diagnosing developmental issues, offering clinical support for pediatric dentistry.

Keywords:
Deep learningDental development stage;Ectopic eruptionEruption disturbancesMixed dentition

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Area of Science:

  • Artificial Intelligence in Dentistry
  • Pediatric Dentistry
  • Radiographic Analysis

Background:

  • Ectopic eruption of permanent teeth is a common concern in pediatric dentistry.
  • Accurate diagnosis of dental development stages and potential eruption anomalies is crucial.
  • Traditional diagnostic methods can be time-consuming and subjective.

Purpose of the Study:

  • To develop a diagnostic model for mixed dentition using a multistage deep-learning network.
  • To predict potential ectopic eruption in permanent teeth by integrating dentition segmentation.
  • To automatically classify dental development stages for enhanced diagnostic capabilities.

Main Methods:

  • Utilized a dataset of 1576 panoramic radiographs from children aged 6-12 years.
  • Employed a deep-learning network for automatic classification of dental development stages and tooth segmentation.
  • Expert diagnoses served as the benchmark for training and evaluating the artificial intelligence (AI) model.

Main Results:

  • The AI model achieved high accuracy in tooth segmentation (IoU: 0.959, Precision: 0.993, Sensitivity: 0.966, F1: 0.979).
  • The model's accuracy in identifying ectopic tooth eruptions was comparable to or surpassed that of dentists.
  • Demonstrated potential in classifying dental development stages against multiple expert standards.

Conclusions:

  • The AI-enabled multistage deep-learning model is effective for diagnosing ectopic eruptions in mixed dentition.
  • The model shows adaptability across multiple diagnostic scenarios.
  • Findings support the development of predictive models for various pediatric dentistry conditions.