Adenoid hypertrophy may lead to pediatric obstructive sleep apnea and mouth breathing. The routine screening of adenoid hypertrophy in dental practice is helpful for preventing relevant craniofacial and systemic consequences. The purpose of this study was to develop an automated assessment tool for adenoid hypertrophy based on artificial intelligence. A clinical dataset containing 581 lateral cephalograms was used to train the convolutional neural network (CNN). According to Fujioka’s method for adenoid hypertrophy assessment, the regions of interest were defined with four keypoint landmarks. The adenoid ratio based on the four landmarks was used for adenoid hypertrophy assessment. Another dataset consisting of 160 patients’ lateral cephalograms were used for evaluating the performance of the network. Diagnostic performance was evaluated with statistical analysis. The developed system exhibited high sensitivity (0.906, 95% confidence interval [CI]: 0.750–0.980), specificity (0.938, 95% CI: 0.881–0.973) and accuracy (0.919, 95% CI: 0.877–0.961) for adenoid hypertrophy assessment. The area under the receiver operating characteristic curve was 0.987 (95% CI: 0.974–1.000). These results indicated the proposed assessment system is able to assess AH accurately. The CNN-incorporated system showed high accuracy and stability in the detection of adenoid hypertrophy from children’ lateral cephalograms, implying the feasibility of automated adenoid hypertrophy screening utilizing a deep neural network model.

Bitewings are commonly used radiographs for visualizing teeth and various dental conditions. Manual labeling and diagnosis on bitewings for chart filing are time-consuming and prone to observer-dependent variations. This multi-center study proposes a deep learning (DL) approach to automate comprehensive chart filing of bitewings. A total of 1045 bitewings from Germany and The Netherlands were used for training and validation, and 216 from Slovakia for external testing. Annotations were performed by two dentists, one PhD researcher, and one caries expert. Hierarchical Mask DINO was developed for multi-class hierarchical end-to-end instance segmentation. Unmodified Mask DINO, SparseInst, and Mask R-CNN were used for comparison. Model performance was evaluated using F1-score, sensitivity, specificity, precision, mean average precision (mAP), and area under receiver operating characteristic curve (AUC). Mask DINO models exhibited high effectiveness for tooth segmentation and labeling, achieving precision, sensitivity, and F1-scores above 0.96. Hierarchical Mask DINO outperformed the other models in dental finding classification. F1-scores for implant, crown, pontic, filling, root canal treatment (RCT), caries lesion, and calculus deposit were 0.944, 0.918, 0.952, 0.956, 0.988, 0.749, and 0.758, respectively, with specificities all above 0.95. This study presented a DL-based approach for comprehensive assessment and diagnosis of bitewings, underlining its potential to enhance the efficiency and accuracy of chart filing in dental practice. The proposed model provided fully automated tooth segmentation and numbering, along with comprehensive segmentation of dental conditions. Dental professionals can benefit from this model for reducing manual workload and enhancing clinical diagnosis.

Considering the importance of distinguishing between primary and permanent teeth in children with mixed dentition, this study aimed to develop and evaluate an automated method for segmenting and labelling primary and permanent teeth in digital impressions. 716 digital impressions from 351 patients with primary or mixed dentitions were collected from the Netherlands, Brazil, and the 3DTeethSeg22 challenge dataset. The scans were annotated with tooth segmentations and primary and permanent teeth FDI numbers. A deep learning model was applied that combined large-context predictions for tooth labelling with high-resolution predictions for tooth segmentation. Using the collected scans, the model was trained and evaluated with five-fold cross-validation for tooth detection (F1-score), tooth segmentation (Dice score), and tooth labelling (macro-F1). Additionally, the model was trained and evaluated using the train-test split of the 3DTeethSeg22 challenge dataset. The developed model achieved highly effective results for tooth detection (F1-score = 0.996), tooth segmentation (Dice = 0.969), and tooth labelling (macro-F1 = 0.989). Moreover, a digital impression was processed in under two seconds on average. Furthermore, the proposed method outperformed the top-ranked 3DTeethSeg22 challenge submission (score = 0.954 vs. 0.976) and was particularly effective for tooth labelling (tooth identification rate = 0.910 vs. 0.955). Failure cases revealed mistakes for unusual dental conditions or ambiguous tooth eruption patterns. A highly effective algorithm for tooth segmentation was developed to differentiate between primary and permanent teeth in digital impressions. This fast and accurate model can benefit dentists in documenting children's teeth during the mixed dentition stage. The algorithm provides an accurate and reliable tool for AI-assisted identification and numbering of primary and permanent teeth in digital impressions obtained from children with mixed dentition, thereby enhancing clinical workflow, improving treatment planning accuracy, and facilitating communication with patients and caregivers.