Prostate MR AI (VA10A)

K241770

Siemens Healthcare GmbH · cleared 2025-03-05 · product code QDQ · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“Prostate MR AI is a plug-in Radiological Computer Assisted Detection and Diagnosis Software device intended to be used”
AlgorithmArtificial intelligence algorithm trained on a database of prostate MR image series acquired according to the PI-RADS standard (non-contrast T2W and DWI image series), and corresponding radiological and/or biopsy findings.
source quote (p.10)
“Artificial intelligence algorithm trained on a database of prostate MR image series acquired according to the PI-RADS standard (non-contrast T2W and DWI image series), and corresponding radiological and/or biopsy findings.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (3)

Bench

n=222 cases · 10 site(s)

endpoints: Dice score; normalized volume difference

Bench

n=105 cases · 6 site(s)

endpoints: case level sensitivity; false positive rate per case; accuracy of the PI-RADS classification

Reader study (MRMC)

n=340 cases · 2 site(s)

endpoints: case-level diagnostic performance of aided and unaided reads using the reader-provided case-level LoS (RLoS)

standards: ISO 14971 Third Edition 2019-12, IEC 62304 Edition 1.1 2015-06 CONSOLIDATED VERSION, IEC 82304-1 Edition 1.0 2016-10

Reported performance (4 observations)

sensitivity0.6CI [0.53, 0.68]
source quote (p.17)
“In the fully inclusive analysis, the average sensitivity/specificity of the Readers at a case-level RLoS threshold of ">= 3 was ... 0.60 (95% C.I.: [0.53, 0.68]) ... in aided reading.”
specificity0.73CI [0.65, 0.80]
source quote (p.17)
“In the fully inclusive analysis, the average sensitivity/specificity of the Readers at a case-level RLoS threshold of ">= 3 was ... 0.73 (95% C.I.: [0.65, 0.80]) in aided reading.”
aurocas written: “auc”0.701
source quote (p.17)
“In the fully inclusive analysis, the average area under the ROC (Receiver Operating Characteristic) curve (AUROC) improved from 0.6758 in unaided reading to 0.7010 in aided reading, with a difference of 0.0252 (95% C.I. [0.0011, 0.0493]; P=0.040).”
agreement_kappaas written: “Fleiss' Kappa (aided)”0.371CI [0.326, 0.411]
source quote (p.18)
“Fleiss' Kappa for interreader agreement in per-case PI-RADS scores was ... 0.371 (95% C.I.: [0.326, 0.411]) for aided reads”

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/K241770