ScanBodAI

Clinical Guide

How to Identify an Unknown Scan Body

When a scan arrives with no implant system documentation, the lab has to figure out which scan body is in the file before production can begin. This guide explains how scan bodies are geometrically distinguished from one another, why the manual identification workflow breaks down, and how automated STL comparison narrows the candidates.

Why labs receive cases with unknown scan bodies

Dental labs process cases from multiple clinics, each using different intraoral scanners and implant systems. The work order may say "Straumann" — but the scan file itself contains no embedded metadata about which implant platform was actually used. The scan body in the STL is a specific model with specific tolerances. Getting it wrong — designing the restoration to the wrong platform, or ordering the wrong abutment — means the case must be remade.

This problem compounds at scale. With 3,600+ searchable reference entries across 60+ manufacturers in our reference library — each available in narrow, regular, and wide platform variants — manual identification relies almost entirely on institutional memory and catalog lookups. When that memory is wrong, the remake follows.

What makes scan bodies geometrically distinguishable

Each scan body is a precision-manufactured component, and its geometry encodes the implant system it belongs to. The key distinguishing features are:

Top-cap shape

The anti-rotation geometry at the coronal end — hexagonal, octagonal, trilobular, or proprietary — varies between manufacturers and is the primary connection identifier. Straumann BLT uses a different top-cap profile than Straumann BLX, for example, even though both are Straumann products.

Platform diameter (NP / RP / WP)

Narrow Platform (NP), Regular Platform (RP), and Wide Platform (WP) describe the diameter at the implant-to-abutment interface. Platform diameters differ between narrow and regular platforms by measurable amounts visible directly in the STL geometry — and they determine which prosthetic components are compatible.

Taper angle

The angle at which the body widens from base to shoulder. Conical connections (Morse taper) have a gradual, precise taper. Internal hex connections have a near-vertical wall. The taper ratio is a reliable discriminating feature across platforms.

Height and diameter profile

The full-length cross-sectional diameter profile — measured at regular intervals from base to apex — is unique enough to distinguish even closely related models from the same manufacturer. A Straumann BLT NC and a Neodent GM NF have similar top-cap diameters but very different mid-body profiles.

Apex shape

Whether the top is flat, domed, or has a specific seating feature distinguishes within-brand variants. Some manufacturers use a distinctive apex shape that differs meaningfully from most others — a feature that survives even partial tissue coverage.

Anti-rotation geometry

Features machined into the base that prevent rotation after seating — flat faces, grooves, slots, or indexing features — vary in number, depth, and angular position and provide a strong fingerprint even when other dimensions are similar.

Taken together, these features create a geometric fingerprint that helps separate scan body models, though some remain very similar — and that fingerprint is present in the STL file even before anyone looks at it manually.

The manual identification workflow — and where it fails

The current industry standard for identifying an unknown scan body involves several manual steps, each of which introduces error:

  1. Open the STL in a viewer and visually inspect the top-cap shape and approximate diameter.
  2. Cross-reference against a catalog — either a printed reference book, a manufacturer's PDF library, or an online catalog — and select the most likely match.
  3. Open the CAD software, import the suspected scan body from its internal library, and manually align it to the scanned version by placing alignment points.
  4. Visually verify the fit. If it doesn't match well, repeat with the next candidate.

This workflow has well-documented failure modes:

Why manual identification breaks down

  • Visually similar bodies from different manufacturers — particularly in the 3.5–4.0 mm narrow-platform range — are routinely confused. Top-cap shapes that look identical to the eye differ by tenths of a millimeter that determine component compatibility.
  • The catalog lookup step depends on the technician knowing where to look. Labs that process cases from 10+ implant systems cannot maintain accurate institutional memory for every platform.
  • Manual alignment point selection in CAD software typically requires the user to pre-identify the scan body before importing it. If the pre-selection is wrong, the manual alignment step confirms a false match.
  • Partially exposed scan bodies — where soft tissue covers part of the body geometry — make visual inspection and manual alignment significantly less reliable. Accuracy of identification and alignment degrades when a scan body is only partially exposed; this is a recognized limitation across the industry, and manual verification is considered essential even in guided digital workflows.
  • There is no checksum. A technician who selects the wrong scan body from the library and aligns it visually gets no objective failure signal — the CAD software proceeds.

What the major CAD platforms require

Common CAD workflows typically ask the user to choose a scan body from a library and seed alignment by hand, followed by visual verification. The software positions the chosen body where the user places it.

That works when the model is known. When it is not, the library choice itself becomes the open question.

ScanBodAI compares the scan geometry with references automatically and returns ranked candidates, with no manual point-picking or library pre-selection.

How automated STL fingerprinting works

The engine reads the STL geometry directly and measures the scan body across many quantitative features — its profile, taper, platform dimensions, and anti-rotation geometry among them — assembling a geometric fingerprint that is matched against the reference library. No pre-selection is required from the user.

The result is returned with a confidence level: CONFIRMED (strong geometric match with a wide margin over the next candidate), POSSIBLE (plausible match but some ambiguity — verify against the work order), LOW (no high-confidence match found — the body may not yet be in the library), or ABSTAIN when the engine cannot establish an identity. Full-arch results stop at POSSIBLE.

Accuracy limits: The engine returns ranked candidates with a confidence tier, not a guarantee. Real-scan accuracy has not been independently validated, and a partially tissue-covered body or a model outside the library may still return a candidate that looks plausible. The result is a signal to assist the clinician or lab technician — it does not replace verification against the work order and the patient record.

How to identify an unknown scan body with ScanBodAI

01

Export the STL from your scanner software

In 3Shape, exocad, or any other CAD platform, export the scan body as an STL or OBJ file. The raw mesh is all that is needed — no alignment, no pre-selection.

02

Upload to ScanBodAI

Drop the file into the ScanBodAI identification tool. Arch scans with multiple scan bodies are supported — the engine segments each body and compares them independently.

03

Review the result

The engine returns a ranked list of candidates with confidence levels and the geometric features that drove the ranking. Where compatibility data is available, results link related components and cross-brand references. Verify with the manufacturer before ordering.

04

Verify against the work order

Cross-check the identified manufacturer, platform, and model against what the dentist ordered. A mismatch here — before production starts — prevents the remake.

Try it now — free

Upload an STL and review ranked candidates.

No account required for the demo. No manual point-picking. No library pre-selection.

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Technical Deep-Dive

Why Partial and Tissue-Covered Scan Bodies Are Hard to Identify

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