Overjet CBCT Assist

K251514

Overjet, Inc. · cleared 2025-12-05 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“Overjet CBCT Assist is a software for the analysis of dental and craniomaxillofacial Cone Beam Computed Tomography (CBCT) images. The software utilizes artificial intelligence/machine learning algorithms to provide automated segmentations, user-delineated or automated measurements, and 2D/3D visualizations.”
Algorithmartificial intelligence/machine learning algorithms
source quote (p.4)
“The software utilizes artificial intelligence/machine learning algorithms to provide automated segmentations, user-delineated or automated measurements, and 2D/3D visualizations.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (2)

Bench

sample size not stated

standards: FDA guidance “General Principles of Software Validation" (FDA, January 2002), IEC 62304:2006+A1:2015

Retrospective clinical

n=100 scans

endpoints: Instance-level sensitivity for dental anatomy, restorative structures, and maxillofacial anatomy; Instance-level specificity for restorative structures; Dice similarity coefficient for all segmented structures; Measurement accuracy (Mean Absolute Error (MAE) and Root Mean Square Error (RMSE)) for automated linear measurements displayed in MPR views; Tooth-level sensitivity and accuracy for tooth numbering

Reported performance (3 observations)

sensitivity87CI 95% CI (82.3%, 91.2%)
source quote (p.11)
“the observed instance-level sensitivity for restorative structures was 87.0% with 95% CI (82.3%, 91.2%)”
diceas written: “Dice similarity coefficient”stated without value
source quote (p.11)
“Dice scores for segmented structures passed their individually associated thresholds across all evaluated classes.”
accuracyas written: “Measurement accuracy (MAE and RMSE)”stated without value
source quote (p.11)
“Measurement accuracy for linear distances also met the target thresholds for MAE and RMSE.”

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

0
recalls in product code, 24mo
3
MAUDE reports in code, 12mo
—
vs code's own 3-yr baseline
0
drift signals on this device

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device); a recall is shown as device-attributed only when the recall record itself lists this clearance number. 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).

Applicable FDA guidance — what the submission is measured against

FDA guidance documents and guiding principles applicable to 510(k) AI/ML devices in the Radiology panel. A curated reference index, not legal or regulatory advice — each item states its own status, and a draft is never binding.

Applicability is derived from the device's FDA advisory panel and pathway — cross-cutting guidances apply to every AI/ML device; panel-specific ones are flagged. Titles, dates, and links verified against fda.gov as of July 2026.

Constat Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: constat.dev/precedent/device/K251514