Healthcare / Vision & Diagnostics

Transforming Dental Imaging Workflows with Smart Tooth Segmentation

Industry: Healthcare / Dental Imaging & Diagnostics
Tech stack: PyTorch / Deep Neural Networks / DEXTR

Background

A major dental services provider was managing an ever-growing archive of panoramic mouth scans and intraoral images. These visuals were essential for diagnosis, treatment planning, and long-term patient records — but handling them at scale was becoming increasingly complex.

To streamline how their teams reviewed and labeled scan data, the company sought an AI solution that could make tooth segmentation faster and less dependent on manual precision.

Challenge

Dental scans require clear, accurate identification of individual teeth — a task that typically involves:

Manually marking 20 to 40 points per tooth to trace their outlines.
Relying on trained specialists to do the work, which was time-consuming and inconsistent across users.
Struggling to scale labeling efforts across thousands of images, especially when expanding services to new clinics or digital platforms.

The company needed a way to make this process faster, more consistent, and accessible to a broader range of staff.

Solution

We developed a smart segmentation tool that drastically simplified the annotation process by requiring only four user-selected points to generate a full tooth outline.

Efficient Tooth Segmentation with AI Assistance

Users place four corner-like points around a tooth.
Our model then automatically infers and draws a clean, anatomical outline of the tooth — even in crowded or low-contrast areas of the image.

This made the process significantly faster, while still giving the user control over the final result.

Seamless Integration with Existing Workflows

The tool was designed to work directly with the company’s image archive system, so staff could open, segment, and save annotations without extra steps.

The resulting data could be used for diagnostics, orthodontics, insurance reports, or even training future models with consistent labels.

Result

With the new tool in place, the dental company achieved:

Faster segmentation, cutting annotation time per image significantly.
Better scalability, enabling broader teams — not just specialists — to participate in the labeling process across thousands of images.

Conclusion

By turning a highly manual imaging task into a streamlined, semi-automated process, this project shows how AI can enhance precision, save time, and scale expertise in dental diagnostics. The smart segmentation tool gave the company a practical edge in managing clinical data — and laid the groundwork for smarter, faster imaging workflows across the board.

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