Table 1.

Applied intelligent algorithms in dentistry with strengths and limitations

ML algorithm	Applications in dentistry	Strengths	Limitations
Convolutional neural networks (CNNs)	Dental radiograph analysis Tooth segmentation Caries, fractures, and pathology detection	High accuracy in image-based tasks Automated feature extraction	Requires large labeled datasets Computationally expensive
Recurrent neural networks (RNNs)	Predicting patient outcomes Behavioral pattern analysis in orthodontics	Excellent for sequential data Captures temporal relationships	Struggles with long sequences Susceptible to vanishing gradient issues
Transformer models	Natural language processing (e.g., summarizing dental records) Time-series data in patient monitoring	Handles long dependencies well Scalable to various datasets	Requires extensive computational resources
Support vector machines (SVMs)	Classification of dental diseases Identifying periodontal conditions	Effective in high-dimensional spaces Works well with smaller datasets	Sensitive to parameter selection Performance depends on kernel choice
Random forest (RF)	Prediction of treatment outcomes Analysis of risk factors in dental health	Handles mixed data types Robust to overfitting	Less interpretable compared to simpler models
U-Net	Dental X-ray image segmentation Identifying dental pulp and canal morphology	Effective for biomedical image segmentation Handles small datasets well	Limited in handling large-scale datasets High memory usage
Gradient boosting (e.g., XGBoost)	Diagnosis of dental pathologies Predicting implant success	High prediction accuracy Works well with structured data	Computationally intensive for large datasets
Long short-term memory (LSTM)	Predicting treatment responses over time Analyzing patient records for personalized care	Mitigates vanishing gradient issues Effective for temporal sequences	Resource-heavy for long sequences May require extensive tuning
DenseNet (dense networks)	Caries detection Analysis of orthodontic outcomes	Efficient parameter usage Reduces overfitting with feature reuse	Memory intensive Can struggle with very complex datasets
Generative adversarial networks (GANs)	Generating synthetic dental radiographs Augmenting training datasets	Effective in creating high-quality synthetic data	Training can be unstable Requires careful tuning
Capsule networks (CapsNets)	Analysis of dental CBCT scans Identification of root canal structures	Preserves spatial hierarchies Robust to image distortions	Computationally expensive Difficult to train
K-Nearest neighbors (K-NN)	Patient classification Predicting dental disease trends	Simple to implement Requires no training phase	Inefficient with large datasets Sensitive to noisy data
Autoencoders	Image denoising in radiographs Feature extraction for complex dental data	Reduces dimensionality effectively Useful for unsupervised learning	Risk of losing important information Requires fine-tuning
Reinforcement learning (RL)	Optimization of treatment planning Robotic-assisted surgeries	Learns from interactions Adaptive decision-making	Requires extensive training environments May not generalize well in unseen scenarios
Deep belief networks (DBNs)	Classifying dental materials Predicting material failure	Captures hierarchical features Useful for unsupervised learning	Training is computationally expensive Prone to overfitting in small datasets
Bayesian networks	Risk analysis for periodontal diseases Probabilistic reasoning in diagnosis	Incorporates uncertainty Interpretable results	Requires domain knowledge for prior probabilities Limited scalability
Natural language processing (NLP) models (e.g., BERT)	Processing patient narratives Extracting information from dental records	Excels in text analysis tasks Pretrained models available for fine-tuning	Requires significant data preprocessing Computationally intensive