Reliability of a Deep Learning Model in Predicting Permanent Maxillary Canine Eruption for Preventive Orthodontics: A...

Background/Objective: The management and prevention of impacted maxillary canines is both an art and a science. These important teeth serve both aesthetic and functional purposes, so they demand meticulous evaluation. Deep learning model tools are game-changing technologies, elevating diagnostic capabilities and therapeutic planning. This study comprised a comparative analysis of the prediction of permanent maxillary canine eruption as a preventive measure between experts and a deep learning model. Methods: In this retrospective cross-sectional study, 2230 panoramic radiographs of patients aged 9-14 years were analyzed to assess the patterns of eruption of unerupted maxillary permanent canines (UPMCs). The study images were classified into three sectors according to the modified Ericson and Kurol sectors. Data preprocessing techniques were used to prepare images for the deep learning model by using a DenseNet121-based Convolutional Neural Network (CNN). The data were split into training and testing sets to train the AI to predict sectors. The deep learning model's predictions were evaluated using sector accuracy, precision, recall, and F1 score. Results: The sample included 796 (35.6%) men and 1434 (64.3%) women. The mean age of the participants was 12.2 ± 1.60 years. For sector 1, the right side of UMPCs, the AI reported an accuracy of 99.55% and perfect precision at 100%, while for the left side, accuracy and precision were 99.24% and 100.00%, respectively. For sector 2, prediction on the right side, the performance accuracy values reached 99.73%, and the precision was 98.15%. For the left side of UMPCs, the prediction accuracy was 98.79%, and precision was 94.10%. Regarding sector 3, the right side saw 99.82% accuracy and 98.83% precision. For the left side, the AI achieved an overall accuracy of 99.46% and precision of 99.72%. Conclusions: The deep learning-based system significantly reduced the time and human resources required for landmark identification and parameter generation, making the diagnostic process more efficient in interceptive/preventive orthodontics.