Overjet Caries Assist

K222746

Overjet, Inc. · cleared 2023-03-27 · 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.”
AlgorithmThe machine learning system with the Decision Layer processes bitewing and periapical radiographs and annotates suspected carious lesions. It is comprised of four modules: Image Preprocessor Module, Tooth Number Assignment Module, Caries Module (segments carious lesions using an ensemble of 3 Instance Segmentation models), and Post Processing.
source quote (p.4)
“The machine learning system with the Decision Layer processes bitewing and periapical radiographs and annotates suspected carious lesions. It is comprised of four modules: Image Preprocessor Module - This module performs two functions: Resizes and normalizes the images Evaluates the incoming radiograph and predicts the image type as Bitewing, Periapical, or other. Any images classified as “other” are not processed. 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 model segments carious lesions using an ensemble of 3 Instance Segmentation models. Post Processing - The overlap of tooth masks from the Tooth Number Assignment Module and carious lesions from the Caries Module are used to assign specific carious lesions to a specific tooth. The Post Processing 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 addressedNo

Validation studies (3)

Retrospective clinical

n=2,607 images

endpoints: Sensitivity; Specificity

standards: FDA's "Guidance for Industry and Food and Drug Administration Staff Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data - Premarket Notification [510(k)] Submissions Document" issued on 03 Jul 2012, “Clinical Performance Assessment: Considerations for Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data in Premarket Notification (510(k)) Submissions” Guidance issued January 2020

Bench

sample size not stated

endpoints: Dice coefficient

Reader study (MRMC)

n=660 images

endpoints: Sensitivity; Specificity; Weighted Alternative Free Response Receiver Operating Characteristic (WAFROC); Dice scores

Reported performance (5 observations)

sensitivity0.766CI 73.8%, 79.4%
source quote (p.8)
“For bitewing images, overall standalone sensitivity was 76.6% (73.8%, 79.4%).”
specificity0.991CI 98.9%, 99.2%
source quote (p.8)
“Overall specificity was 99.1% (98.9%, 99.2%) for bitewing images”
aurocas written: “auc”0.785CI 0.746, 0.822
source quote (p.11)
“On bitewing images, for the average of all readers, AUC increased from 0.729 (0.696, 0.761) to 0.785 (0.746, 0.822), for an increase in AUC of 0.055 (0.033, 0.079) unassisted to assisted.”
diceas written: “Mean Dice score (bitewing, primary caries)”0.77CI 0.76, 0.78
source quote (p.9)
“For bitewing images, the mean Dice score was 0.77 (0.76, 0.78) for primary caries”
diceas written: “Mean Dice score (periapical, primary caries)”0.79CI 0.78, 0.81
source quote (p.9)
“For periapical images, the mean Dice score was 0.79 (0.78, 0.81) for primary caries”

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
1
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

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/K222746