Annalise Enterprise

K250831

Annalise-AI · cleared 2025-04-23 · product code QFM · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.7)
“Annalise Enterprise is a software workflow tool which uses an artificial intelligence (AI) algorithm to identify suspected findings on chest X-ray studies in the medical care environment.”
Algorithmconvolutional neural network trained using deep-learning techniques
source quote (p.7)
“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 (1)

Retrospective clinical

n=3,252 cases · 4 site(s)

endpoints: AUC (95% CI) for Pneumothorax; AUC (95% CI) for Tension pneumothorax; AUC (95% CI) for Pneumoperitoneum; AUC (95% CI) for Pleural effusion; AUC (95% CI) for Vertebral compression fracture; Triage effectiveness (turn-around time)

standards: FDA's Guidance for Industry and FDA Staff, “Content of Premarket Submissions for Device Software Functions - Guidance for Industry and Food and Drug Administration Staff", June, 2023.

Reported performance (6 observations)

aurocas written: “Pneumothorax AUC”0.984CI (0.976, 0.990)
source quote (p.11)
“0.984 (0.976, 0.990)”
aurocas written: “Tension pneumothorax AUC”0.989CI (0.984, 0.994)
source quote (p.11)
“0.989 (0.984, 0.994)”
aurocas written: “Pneumoperitoneum AUC”0.987CI (0.976, 0.994)
source quote (p.11)
“0.987 (0.976, 0.994)”
aurocas written: “Pleural effusion AUC”0.977CI (0.969, 0.984)
source quote (p.11)
“0.977 (0.969, 0.984)”
aurocas written: “Vertebral compression fracture AUC”0.972CI (0.960, 0.982)
source quote (p.11)
“0.972 (0.960, 0.982)”
time_to_resultas written: “Triage turn-around time”42.3CI (95% CI: 41.2, 43.4) seconds
source quote (p.12)
“The results demonstrated a triage turn-around time of 42.3 (95% CI: 41.2, 43.4) 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 2026-07-08.

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/K250831