Videa Caries Assist

K213795

VideaHealth, Inc · cleared 2022-04-21 · product code MYN · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“Videa Caries Assist (VCA) software is a cloud-based AI-powered medical device for the automatic detection of carious lesions in dental radiographs. The device itself is available as a service via an API (Application Programming Interface) behind a firewalled network. Provided proper authentication and a bitewing image, the device returns a set of bounding boxes representing the carious lesions detected.”
AlgorithmSupervised Deep Learning
source quote (p.6)
“Development Technology Supervised Deep Learning”
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

n=1,034 images · 10 site(s)

Reader study (MRMC)

n=226 images · 10 site(s)

endpoints: determine whether the diagnostic accuracy of readers aided by VCA is superior to reader accuracy when unaided by VCA, as determined by the AFROC Figure of Merit (AFROC FOM)

Reported performance (4 observations)

sensitivity70.8CI (68.0, 73.7)
source quote (p.7)
“Overall average Se - image-based (%) 70.8 (68.0, 73.7)”
aurocas written: “auc”0.74CI (0.721, 0.760)
source quote (p.7)
“The standalone overall average Alternative Free-response Receiver Operating Characteristic Figure of Merit (AFROC FOM) was found to be 0.740 (95% confidence interval: 0.721, 0.760)”
ppvas written: “Overall average PPV - image-based (%)”59.5CI (56.5, 62.5)
source quote (p.7)
“Overall average PPV - image-based (%) 59.5 (56.5, 62.5)”
ppvas written: “Overall average PPV (%) - lesion-based (pooled)”64.9CI (62.3, 67.6)
source quote (p.7)
“Overall average PPV (%) - lesion-based (pooled) 64.9 (62.3, 67.6)”

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
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/K213795