Viz SDH

K220439

Viz.ai, Inc. · cleared 2022-07-25 · product code QAS · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
Viz SDH is a software-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to a specialist, such as a neurovascular specialist or neurosurgeon, independent of the standard of care workflow. The system automatically receives and analyses non-contrast CT (NCCT) studies of patients for image features that indicate the presence of a subdural hemorrhage (SDH) using an artificial intelligence algorithm, and upon detection of a suspected SDH, sends a notification so as to alert a specialist clinician of the case. Viz SDH is a combination of software modules that consists of an image analysis software algorithm and mobile application software module.
Algorithmartificial intelligence machine learning (AI/ML) software algorithm
source quote (p.4)
The Viz SDH image analysis software algorithm is an artificial intelligence machine learning (AI/ML) software algorithm that analyzes non-contrast CT images of the head for a subdural hemorrhage.
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (1)

Retrospective clinical

n=542 scans · 3 site(s)

endpoints: Sensitivity >= 80%; Specificity >= 80%

Reported performance (4 observations)

sensitivity0.94CI 90% - 97%
source quote (p.7)
Sensitivity and specificity were 94% (90% - 97%) and 92% (89% - 95%), respectively.
specificity0.92CI 89% - 95%
source quote (p.7)
Sensitivity and specificity were 94% (90% - 97%) and 92% (89% - 95%), respectively.
aurocas written: “auc0.96
source quote (p.7)
In addition, the area under the receiver operating characteristic curve (AUC) was 0.96, demonstrating the clinical utility and potential benefits of the classifier based on the imaging study results.
time_to_resultas written: “Average time to alerting a specialist1.15CI ±0.57 minutes
source quote (p.8)
In the study, the average time to alerting a specialist was 69.1±34.3 sec (1.15±0.57 minutes), which is comparable to the average time to notification seen in the Viz ICH of 1.15±0.83 minutes.

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
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) K221100 (decision 2022-08-29) from Viz.ai, Inc. for a matching device line ("Viz RV/LV") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K221100

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).

RIGOR™ Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: radar.healthai.com/precedent/device/K220439