AI Scan Quality Control
Evaluate dental STL scans using AI-powered analysis before they enter production. Three evaluation engines — native AI, Movix, and BiteFinder — provide comprehensive defect detection, mesh validation, hole analysis, occlusal evaluation, and region-of-interest painting with interactive 3D brush tools.
Native AI Evaluation
Leveonn's built-in AI engine scores scans across multiple configurable criteria. Set weights per criterion, define pass/fail thresholds, and let the AI deliver consistent, objective quality verdicts on every scan.
Multi-criterion scoring with configurable weights per evaluation category
Overall pass/fail verdict with detailed per-criterion breakdown
Configurable evaluation templates for different scan types
Historical score tracking and trend analysis per patient
Batch evaluation mode for processing multiple scans simultaneously
Movix Integration
Deep integration with Movix's dental scan analysis platform. Create cases, upload STLs, and run a full suite of validation tasks — mesh validation, hole detection, defect analysis, and hyperocclusion evaluation — all from within Leveonn.
Automated case creation and STL upload via presigned URLs
Validation task: mesh integrity and surface quality checks
Hole detection task: identifies gaps and missing geometry
Defect analysis task: locates surface defects and anomalies
Hyperocclusion task: evaluates occlusal contact accuracy
Run-all mode for batch execution of every task type
3D viewer link generation for detailed visual review in Movix
BiteFinder Integration
Automated bite analysis powered by BiteFinder's specialized engine. Upload scan files, configure analysis parameters, and retrieve results — all with automatic status polling and completion detection.
Case creation with configurable file types and feature parameters
Presigned URL upload proxy for secure, large file transfers
Automatic status polling with completion detection
Analysis results retrieval with downloadable output files
Per-case job configuration JSON for customized analysis parameters
Local case tracking in Firestore for real-time UI state
3D STL Viewer & ROI Painting
A full Three.js-powered 3D viewer built directly into the platform. Rotate, zoom, and inspect scans in real-time. Paint regions of interest on the mesh surface using a brush tool that selects individual faces for annotation.
Real-time 3D rendering with orbit controls, zoom, and pan
Brush tool for painting regions of interest directly on mesh faces
Face-level selection stored in Firestore subcollection for scalability
Multiple ROI colors for categorizing different region types
Screenshot capture of annotated 3D views for documentation
Smooth performance with large STL files via optimized geometry handling
Batch Upload & Patient Tracking
Process multiple scans at once with batch upload workflows. Track evaluations at the patient level, maintain history across visits, and build a comprehensive quality record for every case.
Drag-and-drop batch upload for multiple STL files
Patient-level scan organization with visit history
Evaluation history with score trends over time
Cloud Storage integration for secure scan archival
Cross-engine comparison showing results from all three evaluation sources
Technician Training Mode
Train your QC team by comparing human evaluations against AI verdicts. Technicians perform manual assessments, then see how their judgment aligns with the AI — building consistency and confidence across your team.
Side-by-side comparison of human vs. AI evaluation scores
Per-technician performance tracking and improvement metrics
Configurable training exercises with selected scan sets
Feedback loop that helps calibrate both human and AI judgments
Training history with progress visualization over time
How it works
The typical workflow from start to finish.
See AI Scan Quality Control in action
Every feature is live and production-ready. Let us walk you through how it fits into your lab.