Data-driven AI platform for dens evaginatus detection on orthodontic intraoral photographs.

Background: The aim of our study was to develop and evaluate a deep learning model (BiStageNet) for automatic detection of dens evaginatus (DE) premolars on orthodontic intraoral photographs. Additionally, based on the training results, we developed a DE detection platform for orthodontic clinical applications.

Methods: We manually selected the premolar areas for automatic premolar recognition training using a dataset of 1,400 high-quality intraoral photographs. Next, we labeled each premolar for DE detection training using a dataset of 2,128 images. We introduced the Dice coefficient, accuracy, sensitivity, specificity, F1-score, ROC curve as well as areas under the ROC curve to evaluate the learning results of our model. Finally, we constructed an automatic DE detection platform based on our trained model (BiStageNet) using Pytorch.

Results: Our DE detection platform achieved a mean Dice coefficient of 0.961 in premolar recognition, with a diagnostic accuracy of 85.0%, sensitivity of 88.0%, specificity of 82.0%, F1 Score of 0.854, and AUC of 0.93. Experimental results revealed that dental interns, when manually identifying DE, showed low specificity. With the tool's assistance, specificity significantly improved for all interns, effectively reducing false positives without sacrificing sensitivity. This led to enhanced diagnostic precision, evidenced by improved PPV, NPV, and F1-Scores.

Conclusion: Our BiStageNet was capable of recognizing premolars and detecting DE with high accuracy on intraoral photographs. On top of that, our self-developed DE detection platform was promising for clinical application and promotion.