AngioWaveNet
K244002Angiowave Imaging, Inc. · cleared 2025-09-10 · product code QIH · Radiology
Premarket evidence — what FDA accepted
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”
source quote (p.5)
“AngioWaveNet is a 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.”
source quote (p.6)
“The AI/ML model at the heart of STEP was trained on a comprehensive dataset of 300 anonymized angiograms, averaging 70 frames each, provided by a large non-profit healthcare organization that operates in Maryland and the Washington, D.C. region.”
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
endpoints: clinical decision impact; ease of visualization; processing success rate; patient-level success criteria; unresolved false positives/negatives
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
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).