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.

Industry AI Solutions: Healthcare, Finance, Retail, Manufacturing

We encounter the same pain points: a general text model doesn’t distinguish medical nomenclature, and a standard object detector confuses “weld seam scratch” with “casing scratch.” Each time these are different defects with different consequences. To avoid this, we build industry-specific solutions on top of general methods, but with deep domain knowledge — from regulatory requirements to data specifics. Over 5 years, we have completed 80+ projects in fintech, healthcare, retail, and manufacturing, and none were without adaptation to a specific business case.

Healthcare: Regulatory Maze and Data Governance

Medical AI differs not in technical algorithms but in a compliance-first approach. Depending on the country of application, the model may be a Class II or III medical device requiring clinical trials (FDA, CE MDR, GOST R). We ensure compliance with these standards at the architecture stage — fixing them post-factum is 10× more expensive.

Medical imaging. Detection on X‑rays, CT, MRI is a mature area. Models on ResNet, EfficientNet, SegFormer achieve AUC 0.94–0.97 on standard tasks (pneumonia on CXR, polyps on colonoscopy). Key issue is generalization: a model trained on data from one scanner manufacturer degrades on another due to differences in preprocessing and artifacts. Solution: domain adaptation via MONAI (Medical Open Network for AI) from NVIDIA, which includes DICOM loading, 3D augmentation, and confidence calibration. TotalSegmentator — for automatic segmentation of 117 structures on CT, production‑ready, Apache 2.0 license.

Clinical NLP. Extracting structured information from clinical records: diagnoses (ICD‑10/11), prescriptions, dates, indicators. medspaCy, scispaCy, MedCAT — specialized NLP libraries with ontologies (SNOMED‑CT, UMLS). Fine‑tuning BioBERT or ClinicalBERT on our data yields F1 0.85–0.92 on NER tasks versus F1 0.65–0.72 for general BERT. We verified this on a project with a regional oncology center — cancer stage extraction accuracy increased by 23%.

Clinical decision support. LLM assistants for clinical decision support are a regulatory gray area. We use an RAG system on top of clinical guidelines (UpToDate, local protocols) with explicit citation for each statement. The model does not diagnose but helps find relevant protocols. Stack: LlamaIndex + pgvector + pubmedbert-base-embeddings + Llama Guard for safety. Data in DICOM/HL7 FHIR, on‑premise deployment mandatory.

Finance: How to Ensure Interpretability of a Scoring Model under Basel IV?

The financial sector is one of the most mature in applying ML, but regulation is maximal. Every model affecting credit decisions falls under Basel IV, EU AI Act, GDPR Article 22. We deliver AI solutions for fintech that satisfy these requirements — in a project for a top‑10 bank we deployed a scoring model where each record required SHAP explanations.

Credit scoring. Gradient boosting (LightGBM, XGBoost) dominates. Neural networks yield +0.5–2% AUC but lose interpretability. Standard: LightGBM + SHAP to explain each decision. Fairness checking is mandatory: Fairlearn or aif360 for auditing disparate impact on protected attributes (age, gender). The default class is 1–5% — with an imbalance of 1:30, a model with 97% accuracy may have recall 0.2. Solution: focal loss, class_weight='balanced', SMOTE + careful validation. In one fintech scoring project, the model reduced credit losses by $2.1 million annually.

Algorithmic trading and risk management. LSTM and Transformer for price forecasting are popular but unstable in production due to non‑stationarity of financial series. A more robust approach: ML for signal generation (classification: up/down over horizon N) with traditional portfolio optimization on top. Backtesting via Zipline‑Reloaded, vectorbt, QuantLib. Proper backtesting is critical — look‑ahead bias kills results. We guarantee a clean experiment: all data at signal time is available in real time.

AML (Anti‑Money Laundering). Graph Neural Networks for analyzing transaction networks is an actively developing area. PyG, DGL for GNN. Task: detect suspicious patterns in transaction graphs (layering, structuring). Recall is more critical than precision — better 10 false alarms than miss one money laundering. In a project for a large payment service, we increased recall by 18% without increasing false positive rate.

Retail and e‑commerce: Recommendation Systems and Demand Forecasting

Recommendation systems. Current architectural standard: two‑tower model for retrieval + ranking with cross‑features. TensorFlow Recommenders or Merlin from NVIDIA for GPU‑accelerated feature processing. For small catalogs (<100k items), LightFM is sufficient. A common mistake is training on implicit feedback without accounting for position bias. Solution: IPW (Inverse Propensity Weighting) or randomized logging on a portion of traffic. Development time for a basic recommendation system is 4–8 weeks, including A/B test.

Demand forecasting and inventory optimization. Hierarchical forecasting: SKU → category → store → region. HierarchicalForecast from Nixtla automatically reconciles forecasts across levels. TFT or N‑HiTS for base forecast, gradient boosting for adjustment on exogenous factors (promotions, weather, events). One retail project led to a 15% reduction in stock‑outs due to precise promotion calibration.

Visual search and size compatibility. CLIP embeddings for image search — deploy in 2–3 weeks: clip‑ViT‑B‑32 or clip‑ViT‑L‑14, Faiss or Qdrant index, REST API. For size recommendation — specific models on return data and reviews with fit indication.

Manufacturing: Quality Inspection and Predictive Maintenance

Quality control and defect detection. CV models for product inspection are one of the most mature industry tasks. YOLOv10 for defect detection, SegFormer for segmentation. Specifics: class imbalance (defects are rare), high recall requirement (missing a defect is worse than false alarm). Typical dataset: 500–2000 defect images + 500–1000 normal. Few‑shot learning via DINO or SAM 2 works with 50–100 annotated examples. We gained experience on an electronics production line — recall 0.95 at FPR 0.03. A predictive maintenance deployment saved a manufacturing client $500,000 per year in unplanned downtime.

Predictive maintenance. Vibration sensors, current sensors, thermocouples → feature extraction → anomaly or mode classification. Models: LSTM‑AE for unsupervised, LightGBM for supervised (if failure history is available). Integration with SCADA/OPC‑UA via opcua-asyncio or MQTT. Key metric: False Negative Rate — a missed pre‑failure is more costly than a false alarm. Threshold tuned to business cost of each error type. Timeline: 3 to 6 months to production.

Digital twin and simulation. Surrogate models — ML models replacing expensive physical simulation. If a CFD simulation takes 6 hours and a surrogate (trained on 10,000 simulations) takes 0.01 seconds, that's 2,000,000× speedup for optimization. SALib for sensitivity analysis, botorch for Bayesian optimization on top of surrogate.

General Principles of Industry AI

Regardless of industry, there are patterns that work everywhere. Data matters more than architecture. In healthcare, 1000 quality labeled images are better than 100,000 poor ones. In manufacturing, 200 real defect examples are more valuable than 10,000 synthetic ones. Compliance‑first design — regulatory requirements are easier to embed into architecture from the start than to add later. Logging, explainability, versioning from day one. Domain expert on the team — an ML engineer without domain knowledge does slowly and error‑prone what an ML engineer plus a doctor/financier/technologist does quickly and correctly.

We guarantee certification to customer requirements (ISO 13485, SOC 2, GDPR) and provide full model documentation (model card, datasheet, compliance report). Our experience: 10,000+ engineering hours and 80+ projects.

Work Process for an Industry AI Solution

1. Domain immersion (2–3 days) — interviews with experts, studying regulatory requirements, auditing available data.
2. MVP design (1–2 weeks) — stack and architecture selection, feasibility assessment.
3. Development and validation (from 4 weeks to 6 months depending on industry) — model training, testing, compliance.
4. Integration and deployment (1–4 weeks) — on‑premise / cloud / edge, documentation, staff training.
5. Support and monitoring — model drift, retraining, SLA.

Estimated timelines:

Cost is calculated individually for each project. Get a consultation — we will evaluate your dataset, regulatory map, and business goals.

Why Choose Our Industry AI Solutions?

• 80+ completed projects in fintech, healthcare, retail, and manufacturing.
• 5 years on the market — proven experience with compliance and deployment.
• Quality guarantee: we ensure target metrics (AUC, recall, latency p99) and provide full documentation.
• Licensed technologies: PyTorch, MONAI, LightGBM, Qdrant — we use open‑source with commercially safe licenses.
• Flexibility: we work as a contractor or as an extension of your team.

Contact us for a free data audit and consultation. Request a proposal with a detailed work plan. We will discuss your task and prepare a commercial proposal.