Segmentron Viewer

K251072

DGNCT, LLC · cleared 2025-09-09 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd

source quote (p.5)

“Segmentron Viewer is a semi-automated software as a medical device (SaMD) for dental image processing and management.”

Algorithmsupervised machine learning algorithm

source quote (p.6)

“Both are software-only, Al-based devices that utilize a supervised machine learning algorithm to provide comparable tools for processing and manipulation of maxillofacial radiographic images.”

Adaptive (vs locked)FDA source did not state this

PCCPFDA source did not state this

Cybersecurity addressedFDA source did not state this

Validation studies (4)

Retrospective clinical

n=126 scans

endpoints: Dice Coefficient (primary endpoint) exceeding the pre-defined performance goal (PG); success on the pre-defined secondary endpoints

Retrospective clinical

n=43 scans

endpoints: Dice Coefficient (primary endpoint) = 0.88 (95% CI: 0.87, 0.89; p < 0.0001) – exceeding the pre-defined PG; success on the secondary endpoints

Retrospective clinical

n=56 scans

endpoints: Dice Coefficients for each anatomical region exceeded their respective pre-defined PGs

Retrospective clinical

n=40 scans

endpoints: accuracy of labels automatically generated by the device

Reported performance (3 observations)

diceas written: “Dice Coefficient (Tooth Segmentation)”0.96CI 95% CI: 0.95, 0.96

source quote (p.7)

“Across all teeth, Segmentron demonstrated strong segmentation agreement with the reference standard, as evidenced by the Dice Coefficient (primary endpoint) exceeding the pre-defined performance goal (PG) with a result of 0.96 (95% CI: 0.95, 0.96; p < 0.0001), as well as success on the pre-defined secondary endpoints.”

diceas written: “Dice Coefficient (Pulp Segmentation)”0.88CI 95% CI: 0.87, 0.89

source quote (p.7)

“Across the pulp of all teeth, Segmentron demonstrated strong segmentation agreement with the reference standard, as evidenced by Dice Coefficient (primary endpoint) = 0.88 (95% CI: 0.87, 0.89; p < 0.0001) – exceeding the pre-defined PG and success on the secondary endpoints.”

accuracyas written: “Labeling accuracy”100

source quote (p.7)

“Across all teeth, pulp, and anatomical structures in all CBCT scans, Segmentron Viewer achieved a labeling accuracy of 100%, demonstrating strong concordance between the labels automatically generated by the device and those determined by an expert radiologist.”

Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.

Predicate network

Postmarket — what happened after clearance

recalls in product code, 24mo

MAUDE reports in code, 12mo

—

vs code's own 3-yr baseline

drift signals on this device

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.

Reimbursement — how devices like this got paid

Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).