Viz Subdural+, Viz SUBDURAL PLUS
K250354Viz.ai, Inc. · cleared 2025-06-10 · product code QIH · Radiology
Premarket evidence — what FDA accepted
source quote (p.4)
“The Viz Subdural+ (Subdural Plus) device is intended for automatic labeling, visualization and quantification of collections in the subdural space from a set of Non-Contrast Head CT (NCCT) images. The software is intended to automate the current manual process of identifying, labeling and quantifying the volume of collections in the subdural space identified on NCCT images.”
source quote (p.7)
“Both the subject and predicate devices use an artificial intelligence algorithm to identify, label and quantify measured quantities in NCCT imaging of the head from images acquired at a single time point. Both the subject and predicate devices use software algorithms that incorporate artificial-intelligence to perform as intended. Both devices' algorithms automatically receive, assess the applicability of received input imaging, and automatically process and measure supported imaging. Both devices' algorithms use similar pipelines with similar steps to measure their indicated structures and both devices' algorithms use deep-learning convolutional neural networks with similar architectures.”
source quote (p.5)
“Viz Subdural+ is a software-only device that uses a locked artificial intelligence machine learning (AI/ML) algorithm to process and analyze non-contrast CT (NCCT) scans of the head to automatically measure the collections in the subdural region in the brain and midline shift.”
Validation studies (1)
Retrospective clinical
n=203 cases · 2 site(s)
endpoints: subdural collection measurement performance (volume); subdural collection measurement performance (thickness); midline shift measurement performance; mean absolute error (MAE)
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).