Dental detection: Datasets & Models

Manually reviewing and charting every tooth across large volumes of patient radiographs is slow, repetitive, and prone to inconsistency between clinicians. In healthcare imaging environments where accuracy and throughput both matter, automating teeth detection lets practitioners spend less time annotating images and more time on diagnosis and patient care. A vision model gives you consistent, reproducible detections across every scan, and can flag structures for a clinician to confirm rather than starting from scratch. This is especially valuable for clinics processing high patient throughput or building software tools that need reliable localization before any analysis step.

Automated dental detection produces consistent bounding boxes across every image, reducing manual charting effort and inter-reviewer variability. It integrates with existing intraoral cameras and radiography hardware, so you can layer detection onto current workflows without new capture equipment. Built on RF-DETR, the workflow scales from a single clinic to enterprise imaging pipelines while maintaining strong accuracy on small, closely-spaced objects like individual teeth.

1. Upload an image: Drop any photo into the widget above to try dental detection for free, right on this page.
2. Preview instant results: The model detects tooth, molar, cavity, dental filling, crown within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
3. Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train RF-DETR on your own images for production accuracy.
4. Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.