Dental Image Annotation for Automated Diagnosis & Tele-Dentistry AI — Case Study
How Shaip delivered precise polygon segmentation of individual teeth across dental X-rays, intraoral camera images, and clinical photographs — built as a production-grade dataset for automated dental diagnosis, treatment planning, and tele-dentistry AI.
Project Overview
As dental AI moves toward automated diagnosis and tele-dentistry deployment, the client needed a clinically grounded annotation pipeline capable of segmenting individual teeth — crown, root, and surrounding tissue — across multiple imaging modalities with consistent dental notation.
Shaip built the end-to-end annotation pipeline covering polygon-precise tooth segmentation, standard dental notation tooth numbering, multi-attribute condition tagging, and visibility flagging — producing model-ready datasets for dental diagnostic AI.
Key Stats
Annotation Type
Polygon Seg.
Tooth Coverage
Full Arch
Condition Tags
5
Imaging Sources
3
Challenges
- Tracing precise crown, root, and tissue margins with polygon segmentation
- Covering incisors, canines, premolars, and molars across both upper and lower arches
- Working across three imaging sources — X-rays, intraoral cameras, and clinical photographs
- Applying standard dental notation for consistent tooth numbering
- Tagging condition status across healthy, decayed, chipped, missing, and filled
Solution
Polygon Tooth Segmentation
Each tooth in every image was individually annotated using precise polygon segmentation, tracing the exact boundary of each tooth structure including the crown, root, and surrounding tissue margins. Polygon density was calibrated to capture organic tooth contours without unnecessary vertex overhead.
Full Arch Coverage
Annotations covered all tooth types — incisors, canines, premolars, and molars — across both upper and lower jaw arches. Every visible tooth received its own polygon and attribute set, ensuring downstream diagnostic models had full-mouth context for treatment planning.
Multi-Modality Imaging Source Handling
The dataset spanned dental X-rays, intraoral camera images, and clinical photographs — each with distinct imaging characteristics. Annotators followed source-specific guidelines to maintain consistent labeling across modalities, ensuring the trained model could generalize across clinical imaging types.
Standard Dental Notation
Each annotated tooth was tagged with its number using standard dental notation, enabling automated diagnostic outputs that integrate directly with existing dental record systems. This notation consistency makes the dataset immediately deployable for clinical workflow integration.
Condition & Visibility Tagging
Beyond polygon placement, each tooth received condition status tagging — healthy, decayed, chipped, missing, or filled — and visibility status — fully visible, partially occluded, or obstructed. This multi-layer tagging supports both detection and diagnostic AI applications.
Project Scope
| Dataset Type | Annotation Method | Imaging Sources | Tooth Coverage | Condition Tags | Notation |
|---|---|---|---|---|---|
| Dental polygon segmentation | Polygon segmentation | X-ray + intraoral + clinical | Full upper + lower arch | 5 (healthy, decayed, chipped, missing, filled) | Standard dental notation |
Outcomes
- Established a clinically grounded dental segmentation pipeline across three imaging modalities
- Standardized standard dental notation tooth numbering for clinical system integration
- Delivered 5-class condition tagging spanning healthy to filled teeth
- Maintained polygon-level precision capturing crown, root, and tissue margins
- Enabled the client’s automated dental diagnosis, treatment planning, and tele-dentistry AI roadmap
Overall, Shaip helped transform a multi-modality dental annotation requirement into a clinically structured, production-ready segmentation pipeline — one capable of supporting automated dental diagnosis, AI-assisted treatment planning, oral health monitoring, and tele-dentistry applications with clinical-grade precision.
Shaip’s polygon segmentation captured exactly what our diagnostic model needed — crown, root, and tissue margins, with proper dental notation throughout. The multi-modality coverage made our model robust across X-ray and intraoral imaging.
– CTO, Dental AI Platform
