A Deep-learning System for Diagnosing Ectopic Eruption

Objectives: To construct a diagnostic model for mixed dentition using a multistage deep-learning network to predict potential ectopic eruption in permanent teeth by integrating dentition segmentation into the process of automatic classification of dental development stages.

Methods: A database was established by reviewing 1576 anonymous panoramic radiographs of children aged 6-12 years, collected at the Stomatology Hospital, xxxxxx. These radiographs were categorised as normal or ectopic eruption, with expert diagnoses serving as a benchmark for training and evaluating artificial intelligence (AI) models. Furthermore, tooth boundaries and dental development stages were manually annotated by three pediatric dentistry experts. The dataset was split into training, validation, and test sets at an 8:1:1 ratio.

Results: The diagnostic performance of the deep-learning model was rigorously evaluated. The model demonstrated accuracy in tooth segmentation, with Intersection over Union, precision, sensitivity, and F1 scores of 0.959, 0.993, 0.966, and 0.979, respectively. Furthermore, its ability to identify tooth ectopic eruptions on panoramic radiographs, when compared to evaluations by three dentists. Based on McNemar's test, the model's specificity and accuracy in identifying ectopic tooth eruptions on the test dataset surpassed that of Dentist 1 (P < 0.05), while no significant difference was observed compared to the other two dentists. Besides, the deep learning model also showed its potential in classifying dental development stages, as tested against three different standards.

Conclusions: The adaptability of the AI-enabled model in this study was demonstrated across multiple scenarios, with clinical validation highlighting its efficacy in diagnosing ectopic eruptions using a multistage deep-learning approach.

Clinical significance: Our findings provide new insights and technical support for the prevention and treatment of abnormal tooth eruption, laying the groundwork for predictive models for other prevalent pediatric dentistry conditions.