Overjet Caries Assist

K212519

Overjet, Inc. · cleared 2022-05-10 · product code MYN · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“OCA is a software-only device which operates in three layers a Network Layer, a Presentation Layer, and a Decision Layer (as shown in the data flow diagram below).”
AlgorithmMachine Learning System comprised of four modules: Image Classifier, Tooth Number Assignment module, Caries module (ensemble of 3 U-Net based models), and Post Processing.
source quote (p.4)
“The Machine Learning System within the Decision Layer processes bitewing radiographs and annotates suspected carious lesions. It is comprised of four modules: Image Classifier - The model evaluates the incoming radiograph and predicts the image type between Bitewing and Periapical Radiograph. This classification is used to support the data flow of the incoming radiograph. As part of the classification of the image type any non-radiographs are classified as “junk” and not processed. These include patient charting information, or other non-bitewing or periapical radiographs. OCA shares classifier and Tooth Number modules with the Overjet Dental Assist product cleared under K210187. Tooth Number Assignment module – This module analyzes the processed image and determines what tooth numbers are present and provides a pixel wise segmentation mask for each tooth number. Caries module – This module outputs a pixel wise segmentation mask of all carious lesions using an ensemble of 3 U-Net based models. The shape and location of every carious lesion is contained in this mask as the carious lesions' predictions. Post Processing - The overlap of tooth masks from the Tooth Number Assignment Module and carious lesions from the Caries Module is used to assign specific carious lesions to a specific tooth. The Image Post Processor module annotates the original radiograph with the carious lesions' predictions.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.12)
“Risk management was conducted according to ISO 14971 which ensured, via a risk analysis, the identification and mitigation of potential hazards. Any potential hazards were controlled via software development and design, verification, and validation testing.”

Validation studies (2)

Standalone

n=352 images

endpoints: Sensitivity; Specificity; Dice score

standards: ISO 14971

Reader study (MRMC)

n=352 images

endpoints: Reader sensitivity; Reader specificity; Dice scores; AFROC AUC

standards: ISO 14971

Reported performance (7 observations)

sensitivity0.72CI 62.9%, 81.1%
source quote (p.9)
“Overall standalone sensitivity was 72.0% (62.9%, 81.1%).”
specificity0.981CI 97.7%, 98.5%
source quote (p.9)
“Overall standalone specificity was 98.1% (97.7%, 98.5%).”
aurocas written: “auc”0.649CI 0.744, 0.820
source quote (p.11)
“For the average of all readers, AUC increased from 0.593 (0.686, 0.743) to 0.649 (0.744, 0.820), for an increase in AUC of 0.057 (0.039, 0.098) unassisted to assisted.”
diceas written: “Mean Dice score (Primary Caries)”0.69CI 0.66, 0.72
source quote (p.10)
“For 66 images containing primary caries, the mean Dice score was 0.69 (0.66, 0.72) with a standard deviation of 0.122).”
diceas written: “Mean Dice score (Secondary Caries)”0.75CI 0.71, 0.79
source quote (p.10)
“For 30 images containing secondary caries, the mean Dice score was 0.75 (0.71, 0.79) with a standard deviation of 0.112.”
sensitivityas written: “Assisted Reader Sensitivity (Overall)”0.762CI 68.4%, 82.6%
source quote (p.11)
“Overall reader sensitivity improved from 57.9% (48.9%, 66.0%) to 76.2% (68.4%, 82.6%) unassisted vs assisted.”
specificityas written: “Assisted Reader Specificity (Overall)”0.984CI 94.5%, 98.8%
source quote (p.11)
“Overall reader specificity decreased slightly from 99.3% (99.1%, 99.5%) to 98.4% (94.5%, 98.8%) unassisted vs assisted.”

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
0
MAUDE reports in code, 12mo
-100%
vs code's own 3-yr baseline
2
drift signals on this device
  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K233738 (decision 2024-03-04) from Overjet, Inc for a matching device line ("Overjet Caries Assist-Pediatric") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K233738

  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K222746 (decision 2023-03-27) from Overjet, Inc. for a matching device line ("Overjet Caries Assist") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K222746

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/K212519