u/SignificantWash6129

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About a year ago, I started building a fairly simple AI system to check the quality of intraoral scans.

The original idea was basically:

Upload a 3D dental scan → detect technical problems → tell the user whether it should pass, be reviewed, or be rescanned.

It has… escalated slightly.

I’m now training what is becoming a native 3D dental foundation model.

Instead of having completely separate models for every dental task, the idea is to build one shared model that develops a deeper internal understanding of dentition.

The same model is learning to understand:

• where individual teeth are
• tooth vs gum segmentation
• FDI tooth identity
• quadrant, side, tooth type and position
• relationships between neighbouring teeth
• upper vs lower arches
• prepared teeth
• dense preparation geometry
• margin localisation
• dense mesh defects
• confidence and uncertainty
• paired upper/lower arch relationships
• occlusal geometry

The important bit is that these aren’t just isolated outputs.

The heads are designed to constrain and support one another.

A tooth shouldn’t just be called an UR6 because one classifier says so. Its arch, side, quadrant, position, neighbouring teeth and sequence should all agree.

The latest mid-training model is already:

• detecting around 97% of teeth
• numbering teeth at roughly 87–92% accuracy on held-out arch data
• producing dense preparation segmentation at around 0.74 IoU with 0.93 recall
• generating pseudo-labels at roughly 0.94 FDI accuracy on the labels it chooses to commit
• reaching around 0.98 binary segmentation IoU on committed pseudo-labels
• correctly refusing to label unfamiliar or out-of-distribution data rather than confidently producing garbage

That last point is probably the part I’m most interested in.

The aim isn’t just to build a model that predicts.

The aim is to build a model that knows when it understands a scan well enough to act as a teacher.

If that works, the model can begin generating high-quality labels for thousands of previously unlabeled scans, creating a pseudo-label flywheel where each generation helps create the training data for the next.

The roadmap now looks roughly like this:

Foundation model → dense defect and margin refinement → paired occlusal intelligence → pseudo-label expansion → downstream dental applications

The first commercial application is still likely to be scan quality control.

But increasingly, I don’t think the real asset is the QC application.

I think the real asset may be the shared 3D dental intelligence underneath it.

Potentially, the same learned representation could support:

• scan QC
• tooth identification
• restorative workflows
• orthodontic measurements
• occlusal analysis
• prep and margin tools
• developer APIs
• applications I haven’t even thought of yet

I’m building this largely on my own, in my spare time, without a traditional software engineering or machine learning background.

My actual background is 20+ years in dental technology and digital dentistry, which means I understand the dental problem far better than I understand why anyone thought it was sensible to let me near a 35-head neural network.

I’m posting this because I’m genuinely curious:

Does the idea of a reusable native-3D dental foundation model make sense to people working in AI, computer vision, dentistry or medical technology?

And perhaps the bigger question:

Why do most dental AI systems still appear to be built as separate task-specific tools rather than around one shared learned representation of dental anatomy?

I’d genuinely love people to challenge the idea.