Brainomix 360 Triage Stroke

K251983

Brainomix Limited · cleared 2025-08-26 · product code QAS · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“Brainomix 360 Triage Stroke is a radiological computer aided triage and notification software indicated for use in the analysis of non-contrast head CT (NCCT) images to assist hospital networks and trained clinicians in workflow triage by flagging and communicating suspected positive findings of head NCCT images for large vessel occlusion (LVO) of the intracranial ICA and M1 or intracranial hemorrhage (ICH).”
Algorithmartificial intelligence algorithm, machine learning algorithms such as advanced non adaptive imaging algorithms, artificial intelligence, and large data analytics, deep learning framework, CNN architecture
source quote (p.5)
“Brainomix 360 Triage Stroke uses an artificial intelligence algorithm to analyze images and highlight cases with detected NCCT LVO or ICH on the Brainomix server on premise or in the cloud in parallel to the ongoing standard of care image interpretation.”
Adaptive (vs locked)No
source quote (p.7)
“The device uses machine learning algorithms such as advanced non adaptive imaging algorithms, artificial intelligence, and large data analytics.”
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.12)
“Brainomix 360 Triage Stroke has been designed to follow the FDA Cybersecurity Guidance and IEC 81001-5-1.”

Validation studies (3)

Retrospective clinical

n=341 cases

endpoints: assessing the performance of Brainomix 360 Triage Stroke in identifying ICH findings in NCCT head images; performance for detection of subarachnoid hemorrhages (SAHs)

standards: 21 CFR, Part 820.30, ISO 14971:2019, IEC 62304:2015

Retrospective clinical

n=267 cases

endpoints: assessing the performance of Brainomix 360 Triage Stroke in identifying NCCT head images containing large vessel occlusion (LVO) or intracranial hemorrhage (ICH)

standards: 21 CFR, Part 820.30, ISO 14971:2019, IEC 62304:2015

Reader study (MRMC)

sample size not stated

endpoints: compare NCCT LVO sensitivity of the device to that of radiologists; expert non-inferiority; non-expert superiority

standards: 21 CFR, Part 820.30, ISO 14971:2019, IEC 62304:2015

Reported performance (10 observations)

sensitivity96.41CI 92.65-98.65%
source quote (p.10)
“sensitivity was 96.41% (95% CI: 92.65-98.65%)”
specificity96.55CI 92.94-98.70%
source quote (p.10)
“specificity was 96.55% (CI: 92.94-98.70%).”
sensitivityas written: “SAH Sensitivity”85.71CI 60.99-97.67%
source quote (p.10)
“sensitivity was 85.71% (CI: 60.99-97.67%)”
specificityas written: “SAH Specificity”96.55CI 80.60-98.87%
source quote (p.10)
“specificity was 96.55% (CI: 80.60-98.87%).”
sensitivityas written: “NCCT LVO Sensitivity”69.64CI 60.65-77.70%
source quote (p.11)
“NCCT LVO performance was observed at 69.64% sensitivity (CI: 60.65-77.70%)”
specificityas written: “NCCT LVO Specificity”89.57CI 82.92-94.36%
source quote (p.11)
“and 89.57% specificity (CI: 82.92-94.36%).”
sensitivityas written: “NCCT ICH Sensitivity (subset)”95CI 84.47-99.29%
source quote (p.11)
“sensitivity: 95.00% [84.47-99.29%]”
specificityas written: “NCCT ICH Specificity (subset)”88.11CI 83.39-91.92%
source quote (p.11)
“specificity: 88.11% [83.39-91.92%]).”
sensitivityas written: “Reader Study LVO Sensitivity (all readers)”47.94CI 37.91-57.97%
source quote (p.11)
“a sensitivity for all readers (experts and non-experts) of 47.94% (CI: 37.91-57.97%).”
sensitivityas written: “Reader Study LVO Sensitivity (non-experts)”47.18CI 33.62-60.75%
source quote (p.11)
“The general radiologists (non-experts) performed with a sensitivity of 47.18% (CI: 33.62-60.75%).”

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
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/K251983