AI System for Dentistry: Diagnostics, Prosthetics, and Orthodontics

How a Radiologist Misses Apical Periodontitis and What CV Does About It

A six-hour shift for a dental radiologist means 40–50 panoramic images. By the end of the day, concentration drops, and even an experienced doctor can miss pathology. We encountered this in three private clinics and one regional polyclinic. Our CV model, trained on 300,000 annotated pantomograms, maintains 0.92 AUROC for caries regardless of time of day. This is a production solution that has been integrated into real workflow.

How AI Analyzes Panoramic Images

A pantomogram (OPG) is the standard exam for general assessment: caries, periodontitis, cysts, impacted teeth, TMJ pathology. The CV task is detection and localization of pathologies on a 2880×1440 px image. We use YOLOv8 or Mask R-CNN on a dataset of annotated OPGs. AUROC for caries: 0.92 (comparable to a radiologist), for apical periodontitis: 0.88. The main problem is class imbalance: healthy teeth outnumber pathological ones by 10:1. Solution: focal loss (γ=2) and oversampling of pathological cases through augmentation (rotation, brightness jitter, elastic deformation). Integration with Dental Image Management System: DICOM server (Orthanc) → CV inference → overlay of annotations on the image in the doctor's web interface. Latency: 1.8 s per OPG.

Periapical Images and CBCT

Periapical X-ray: detection of caries on contact surfaces (often missed during visual inspection). Recall of caries on proximal surfaces: 0.84 vs. 0.71 for dentists without AI (recent meta-analysis, n=12 studies). CBCT: segmentation of teeth and roots for implant planning. 3D segmentation: nnU-Net on CBCT data — DSC 0.94 for teeth, 0.87 for root canals. Automatic measurement of canal length and alveolar bone width.

How AI Helps in Digital Prosthetics

CAD/CAM and AI-Driven Crown Form Generation

After intraoral scanning (iTero, 3Shape Trios) → digital STL model. AI component: automatic generation of crown/veneer/inlay shape considering occlusion, adjacent teeth, and aesthetics. GAN (or Diffusion model) trained on a dataset of clinically approved restorations. On a test dataset of 1200 crowns: 91% of generated forms were accepted by the technician without significant edits vs. 58% with a basic algorithmic approach. Time saved for the dental technician: 35 minutes per crown.

Smile Design

AI simulation of the result before treatment: CV model on the patient's facial photo predicts the final smile after orthodontics or veneers. GAN-based virtual try-on: the patient sees 5 variations of smile design in 3D. Conversion from consultation to treatment agreement increases by 34% when using AI smile simulation (data from clinics that implemented the system).

Orthodontics and Treatment Planning

Cephalometric Analysis

Automatic localization of 20+ key landmarks on lateral cephalogram: YOLOv8 + heatmap regression. Accuracy ≤1.5 mm from manual markup for 17 out of 21 points. Time: 8 seconds vs. 15 minutes manually.

Orthodontic Treatment Planning

Based on cephalometry, dental casts, and photos: ML recommendations on treatment type (braces/aligners, extraction/non-extraction, surgery). Not a final plan — a differential list for the doctor with justification. Agreement of AI recommendations with the plan of an experienced orthodontist: 76%.

Operational Analytics for the Clinic

No-Show Prediction

Missed appointments — average loss of $80–150 per slot. XGBoost on features: day of week, time, procedure type, patient visit history, season, weather, lead time of appointment. AUROC 0.81. Predictive double-booking for high-risk slots or aggressive reminder for high no-show risk patients.

Recall Management

Automatic reminders for preventive visits: personalization based on history (patient X usually ignores SMS, responds better to a call; patient Y comes if reminded 7 days in advance). ML on historical recall response data. Recall show rate: +22%.

AI System Implementation Process in the Clinic

1. Audit of current processes: analysis of workflows, identification of bottlenecks (diagnostics, planning, patient retention).
2. Data collection and annotation: markup of 5000+ images by expert doctors, creation of a dataset with class imbalance addressed.
3. Training and validation: architecture selection (YOLOv8, nnU-Net), hyperparameter tuning, cross-validation on a representative sample.
4. Integration with DICOM/PACS: Orthanc setup, inference pipeline, HIPAA/GDPR compliance.
5. Testing and deployment: A/B testing on 100+ cases, rollout in pilot clinics, monitoring of latency and accuracy.

Estimated Timeline and Scope of Work

Development of a diagnostic module for one type of image takes 4 to 7 months. A full platform including prosthetics and analytics — 8 to 14 months. Timelines depend on data volume and integration with existing systems. Cost is calculated individually.

AUROC (Area Under the Receiver Operating Characteristic) is a metric indicating the quality of binary classification. A value of 0.92 means the model correctly ranks a random positive example higher than a random negative example in 92% of cases. DSC (Dice Similarity Coefficient) is a segmentation metric where 1.0 indicates perfect overlap.

What Is Included in the Work

• Documentation: model card, operation manual, description of metrics and limitations.
• Access: API for integration, Docker images for local deployment, SSH access to inference server if necessary.
• Personnel training: workshop for doctors (2 days) + video instructions for administrators.
• Support: warranty service for 6 months, SLA for incidents (4 hours for critical issues).

Why Choose AI for Your Clinic

Our experience: 5+ years in AI for medicine, 15+ completed projects for dental clinics. We don't just develop models — we integrate them into real workflows, providing measurable gains in accuracy and speed.

Contact us for a demo of AI diagnostics on your images. Order a pilot project — get a prototype in 4 weeks.