Annalise Enterprise CTB Triage Trauma

K231767

Annalise-AI Pty Ltd · cleared 2023-09-22 · product code QAS · Radiology

FDA 510(k) summary for Annalise Enterprise CTB Triage Trauma (K231767)

Constat organizes this clearance into predicate context, validation evidence, reported performance, postmarket signals, and reimbursement links. Each extracted claim stays tied to the public FDA source; missing fields remain explicitly unstated.

Validation studies
2
Cited predicates
1
Performance observations
3
Product code
QAS
Official FDA 510(k) database record ↗FDA 510(k) source PDF ↗

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“Annalise Enterprise CTB Triage Trauma is a software workflow tool which uses an artificial intelligence (AI) algorithm to identify suspected findings on non-contrast brain CT studies in the medical care environment.”
Algorithmconvolutional neural network trained using deep-learning techniques
source quote (p.6)
“Radiological findings are identified by the device using an AI algorithm a convolutional neural network trained using deep-learning techniques.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (2)

Retrospective clinical

n=460 cases · 5 site(s)

endpoints: sensitivity; specificity

standards: FDA's Guidance for Industry and FDA Staff, “Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices”, May 11, 2005

Bench

n=277 cases

endpoints: triage turn-around time

Reported performance (3 observations)

sensitivity89.9CI 83.8, 94.9
source quote (p.9)
“89.9 (83.8, 94.9)”
specificity97.4CI 94.8, 99.5
source quote (p.9)
“97.4 (94.8, 99.5)”
time_to_resultas written: “triage turn-around time”81.6CI 80.3 – 82.9
source quote (p.10)
“The results demonstrated a triage turn-around time of 81.6 (95%CI: 80.3 – 82.9) seconds”

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 2026-08-26.

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.

Related FDA clearances

More AI/ML devices under product code QAS

These are recent clearances in the same FDA product-code category. Shared classification does not by itself make a device an appropriate predicate.

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Constat Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: constat.dev/precedent/device/K231767