Videa Dental Assist

K232384

VideaHealth, Inc. · cleared 2023-12-15 · product code MYN · Radiology

Premarket evidence — what FDA accepted

Device typesamd

source quote (p.6)

“Videa Dental Assist (VDA) software is a cloud-based AI-powered medical device for the automatic detection of the features listed in the Indications For Use statement in dental radiographs. The device itself is available as a service via an API (Application Programming Interface) behind a firewalled network. [...] Not applicable. The subject device is a software-only device. There are no direct or indirect patient-contacting components of the subject device. There are no sterile or reprocessed components.”

Algorithmcloud-based AI-powered medical device for the automatic detection of the features listed in the Indications For Use statement in dental radiographs. The artificial intelligence algorithms were trained with that patient population and with bitewing, periapical and panoramic radiographs. The development technology is Supervised Deep Learning.

source quote (p.6)

“Videa Dental Assist (VDA) software is a cloud-based AI-powered medical device for the automatic detection of the features listed in the Indications For Use statement in dental radiographs. [...] Videa Dental Assist artificial intelligence algorithms were trained with that patient population [...] Videa Dental Assist artificial intelligence algorithms were trained with bitewing, periapical and panoramic radiographs. [...] Development Technology Supervised Deep Learning”

Adaptive (vs locked)FDA source did not state this

PCCPNo

Cybersecurity addressedNo

Validation studies (2)

Bench

n=1,445 images · 35 site(s)

endpoints: sensitivity; specificity; false positive fraction rate; non-lesion fraction; positive predictive value

standards: ISO 14971:2019 Application of Risk Management to Medical Devices., AAMI CR34971:2022 Guidance on the Application of ISO 14971 to Artificial Intelligence and Machine Learning, IEC 62304 Edition 1.1 2015-06 Consolidated Version: Medical Device Software - Software Life Cycle Processes, Good Machine Learning Practice for Medical Device Development: Guiding Principles October 2021., FDA Premarket Assessment of Pediatric Medical Devices Guidance Document (March 24, 2014)

Reader study (MRMC)

n=378 images · 25 site(s)

endpoints: AFROC Figure of Merit (AFROC FOM)

Reported performance (0 observations)

FDA source did not state a quantitative performance metric — non-reporting is itself the signal.

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

-100%

vs code's own 3-yr baseline

drift signals on this device

re_clearance
The FDA AI/ML device list shows a newer 510(k) K251002 (decision 2025-09-19) from VideaHealth Inc. for a matching device line ("Videa Dental AI") — a new clearance for the same line is a change event.
first seen 2026-07-08 · k_number:K251002

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).