AI4CMR v2.0

K252084

Ai4medimaging Medical Solutions S.A. · cleared 2026-02-11 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“AI4CMR software is designed to report cardiac function measurements (ventricle volumes, ejection fraction, indices, etc.) from 1.5T and 3T magnetic resonance (MR) scanners. AI4CMR uses artificial intelligence to automatically segment and quantify the different cardiac measurements. Its results are not intended to be used on a stand-alone basis for clinical decision-making.”
AlgorithmConvolutional Neural Network (U-Net architecture)
source quote (p.10)
“Model Category: Convolutional Neural Network (U-Net architecture)”
Adaptive (vs locked)No
source quote (p.10)
“The model is "locked,” meaning it does not adapt or change with new input data.”
PCCPNo
Cybersecurity addressedNo

Validation studies (1)

Retrospective clinical

n=61 patients · 1 site(s)

endpoints: Segmentation Performance: Dice; Precision; Recall; Agreement With Predicate/Reference Measurements: Total Forward Volume (TFV); Total Backward Volume (TBV); Maximum Velocity (Vmax)

standards: IEC 62304:2006+A1:2015

Reported performance (7 observations)

sensitivity0.95
source quote (p.11)
“Recall: 0.95”
diceas written: “Dice”0.95
source quote (p.11)
“Dice: 0.95”
ppvas written: “Precision”0.96
source quote (p.11)
“Precision: 0.96”
agreement_kappaas written: “Total Forward Volume (TFV) ICC”0.95
source quote (p.11)
“Total Forward Volume (TFV): ICC 0.95”
agreement_kappaas written: “Total Backward Volume (TBV) ICC”0.82
source quote (p.11)
“Total Backward Volume (TBV): ICC 0.82”
agreement_kappaas written: “Maximum Velocity (Vmax) ICC”0.95
source quote (p.11)
“Maximum Velocity (Vmax): ICC 0.95”
diceas written: “mean Dice Similarity Coefficient”0.857
source quote (p.13)
“Technical validation of the AI-based vessel segmentation demonstrated robustness against manual reference annotations (mean Dice Similarity Coefficient of 0.857)”

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
3
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/K252084