Brainomix 360 e-ASPECTS

K243294

Brainomix Limited · cleared 2025-02-14 · product code POK · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“Brainomix 360 e-ASPECTS is a computer-aided diagnosis (CADx) software device used to assist the clinician in the assessment and characterization of brain tissue abnormalities using CT image data.”
Algorithmartificial intelligence algorithm / trained predictive model
source quote (p.4)
“The imaging features are then synthesized by an artificial intelligence algorithm into a single ASPECTS (Alberta Stroke Program Early CT) score.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.13)
“Brainomix 360 e-ASPECTS has been designed to follow the FDA Cybersecurity Guidance and IEC 81001-5-1.”

Validation studies (3)

Bench

n=110 cases

endpoints: absolute bias of the difference between volumes computed and ground truth (< 12 mL); standard deviation (SD) of the difference between volumes computed and the ground truth (< 19 mL); Pearson's correlation between volume contributing to e-ASPECTS and known phantom volumes (> 0.86)

Retrospective clinical

n=137 scans · 3 site(s)

endpoints: Region-level sensitivity; specificity; AUC; Case-level agreement; positive percentage agreement; negative percentage agreement

Reader study (MRMC)

n=140 scans

endpoints: impact of support from Brainomix 360 e-ASPECTS on reader performance; area under the curve (AUC) for readers with and without e-ASPECTS support; sensitivity; specificity; Cohen's Kappa and weighted Kappa

Reported performance (7 observations)

sensitivity0.69CI 56-75%
source quote (p.11)
“Overall performance in 137 showed an area under the curve (AUC) of 83% (95% CI: 80-86%), with a sensitivity of 69% (56-75%) and a specificity of 97% (80-97%).”
specificity0.97CI 80-97%
source quote (p.11)
“Overall performance in 137 showed an area under the curve (AUC) of 83% (95% CI: 80-86%), with a sensitivity of 69% (56-75%) and a specificity of 97% (80-97%).”
aurocas written: “auc”0.83CI 80-86%
source quote (p.11)
“Overall performance in 137 showed an area under the curve (AUC) of 83% (95% CI: 80-86%), with a sensitivity of 69% (56-75%) and a specificity of 97% (80-97%).”
aurocas written: “statistically significant improvement of AUC”0.064
source quote (p.12)
“Comparison of the area under the curve (AUC) for readers with and without e-ASPECTS support showed a statistically significant improvement of 6.4%, from 78% without e-ASPECTS to 85% with e-ASPECTS (p=.03), which was the primary outcome measure of the study.”
sensitivityas written: “improvement in sensitivity (reader study)”stated without valueCI from 61% to 72%
source quote (p.12)
“This was driven by an improvement in sensitivity (from 61% to 72%), and a small improvement in specificity (from 96% to 98%).”
specificityas written: “improvement in specificity (reader study)”stated without valueCI from 96% to 98%
source quote (p.12)
“This was driven by an improvement in sensitivity (from 61% to 72%), and a small improvement in specificity (from 96% to 98%).”
agreement_kappaas written: “Cohen's Kappa and weighted Kappa”stated without value
source quote (p.12)
“Cohen's Kappa and weighted Kappa also improved significantly with versus without e-ASPECTS.”

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/K243294