syngo.CT LVO Detection

K243145

Siemens Medical Solutions USA, Inc. · cleared 2025-04-10 · product code QAS · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“The subject device syngo.CT LVO Detection is an image processing software that utilizes artificial intelligence learning algorithms to support qualified clinicians (Radiologists, Neuroradiologists, Neurologists) in prioritization of CT-angiography images by algorithmically identifying findings suspicious of a large vessel occlusion and providing notification to the user.”
Algorithmartificial intelligence learning algorithms
source quote (p.6)
“The subject device syngo.CT LVO Detection is an image processing software that utilizes artificial intelligence learning algorithms to support qualified clinicians (Radiologists, Neuroradiologists, Neurologists) in prioritization of CT-angiography images by algorithmically identifying findings suspicious of a large vessel occlusion and providing notification to the user.”
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=602 patients · 4 site(s)

endpoints: sensitivity; specificity

standards: IEC 62304, NEMA PS 3.1 - 3.20, ISO 14971, IEC 62366-1, ISO 15223-1, ISO 20417:2021

Reported performance (2 observations)

sensitivity90.6CI 86.8% - 93.3%
source quote (p.8)
“The observed sensitivity and specificity with 95%-confidence intervals were 90.6% [86.8% - 93.3%] and 88.8% [84.7% – 91.9%], respectively, exceeding the predefined acceptance threshold of >80% sensitivity and specificity.”
specificity88.8CI 84.7% – 91.9%
source quote (p.8)
“The observed sensitivity and specificity with 95%-confidence intervals were 90.6% [86.8% - 93.3%] and 88.8% [84.7% – 91.9%], respectively, exceeding the predefined acceptance threshold of >80% sensitivity and specificity.”

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/K243145