AngioWaveNet

K244002

Angiowave Imaging, Inc. · cleared 2025-09-10 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“AngioWaveNet software is intended for use to enhance the visibility of blood vessels, vascular structures, and related anatomical features within angiographic images, which may be clinically useful to the treating physician”
Algorithmartificial Intelligence (AI) and machine learning (ML) system, neural network architecture in the form of an encoder-decoder
source quote (p.5)
“AngioWaveNet spatio-temporal enhancement processing (STEP) is an artificial Intelligence (AI) and machine learning (ML) system designed to enhance the visibility of blood vessels in angiograms using the unique spatial and temporal information contained in the frames of angiographic cines. The Angiowave STEP method employs a neural network architecture in the form of an encoder-decoder, which sequentially takes multiple contiguous frames of an angiogram as input and uses this information to provide enhanced visualization of vessels in the central frame.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.7)
“For cybersecurity we provided a Cybersecurity Risk Management Report, the Threat Model, the Cybersecurity Risk Assessment, the Software Bill of Materials (SBOM), the Assessment of Unresolved Anomalies, Cybersecurity Metrics, Cybersecurity Controls, Architecture Views, Cybersecurity Testing, Cybersecurity Labeling, Cybersecurity Management Plan, and Interoperability information.”

Validation studies (1)

Reader study (MRMC)

n=31 patients · 1 site(s)

endpoints: clinical decision impact on angiographic pathologic determination tasks; ability to improve ease of visualization of diagnostic coronary angiograms

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

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/K244002