Artificial Intelligence Applications in Dental Imaging

Artificial intelligence (AI) is transforming dental imaging by automating and enhancing the interpretation of radiographic data across multiple modalities. Deep learning, and particularly convolutional neural networks, have been employed to detect carious lesions, segment hard and soft tissues on cone-beam computed tomography (CBCT) scans and panoramic radiographs, and classify stages of periodontal disease. These systems offer rapid, consistent and reproducible analyses that can match or exceed human performance in tasks such as tooth numbering, alveolar bone delineation and nerve canal identification. By integrating these tools into clinical workflows, dentists can access quantitative measures of bone loss, precise implant planning and risk assessment for surgical procedures. Globally, AI-powered imaging supports improved diagnostic accuracy, reduced chair time and more personalised treatment planning while alleviating the burden of routine annotation for clinicians. As real-world data accumulates, these technologies are poised to drive advances in tele-dentistry, remote screening programmes and decision support systems in both high- and low-resource environments.

Research from Nature Portfolio

Recent studies have demonstrated a fully automatic AI system for tooth and alveolar bone segmentation from large multisite CBCT datasets. This system achieved radiologist-level accuracy with average Dice similarity coefficients exceeding 91 % for both teeth and bone, while operating hundreds of times faster than manual approaches. A hybrid deep-learning framework has also been developed to detect periodontal bone loss on panoramic radiographs and automatically stage periodontitis according to established clinical criteria. By combining convolutional network-based detection of bone and cemento-enamel junction levels with conventional image processing for percentage analysis, this method shows high correlation with expert diagnoses across entire jaws. Foundational work has further shown that convolutional neural networks trained on panoramic image segments can detect periodontal bone defects with sensitivity and specificity comparable to practising dentists, thereby reducing diagnostic variability and effort.

Research from all publishers

A fully automatic AI segmentation pipeline for oral surgery-related tissues on CBCT images has been introduced, offering adaptive preprocessing based on data distribution histograms. This system yields average Dice scores of 96.5 % for teeth, 95.4 % for alveolar bone, 93.6 % for maxillary sinus and 94.8 % for mandibular canal segmentation, demonstrating substantial gains over existing methods and significant potential to accelerate surgical planning. A comprehensive review of AI applications in dental healthcare highlights how machine learning techniques have been applied to diagnostic imaging, treatment planning and outcome prediction. It emphasises the rapid growth of AI‐assisted caries detection, automated segmentation for orthodontic analysis and predictive models for oral disease risk, while underscoring the need for cautious integration under human supervision to ensure patient safety.

Artificial Intelligence Applications in Dental Imaging publication trend

The graph below shows the total number of articles in artificial intelligence applications in dental imaging across all publications each year (not limited to Nature Index journals).

Technical terms

Convolutional neural network (CNN): A class of deep learning models that applies convolutional filters to images to extract hierarchical features for tasks such as classification and segmentation.

Cone-beam computed tomography (CBCT): A three-dimensional imaging modality that uses a cone-shaped X-ray beam to capture volumetric data of dental and maxillofacial structures.

Dice similarity coefficient: A statistical measure of overlap between two binary segmentation masks, expressed as twice the area of intersection divided by the sum of the areas.

Panoramic radiograph: A two-dimensional X-ray image that captures the entire dentition and supporting structures in a single continuous sweep around the patient’s head.

References

1. How does artificial intelligence impact digital healthcare initiatives? A review of AI applications in dental healthcare. International Journal of Information Management Data Insights (2023).
2. Fully automatic AI segmentation of oral surgery-related tissues based on cone beam computed tomography images. International Journal of Oral Science (2024).
3. Deep Learning for the Radiographic Detection of Periodontal Bone Loss. Scientific Reports (2019).
4. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Scientific Reports (2019).
5. Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis. Scientific Reports (2020).
6. A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images. Nature Communications (2022).
7. Automated detection of third molars and mandibular nerve by deep learning. Scientific Reports (2019).