MPXA-2000

K222036

Medipixel, Inc. · cleared 2023-03-22 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“MPXA-2000, the stand-alone application, is cardiovascular image analysis software for the viewing and quantification of X-ray angiographic images of the coronary arteries.”
AlgorithmDeep-learning (Classification and Segmentation of vessels) and Computer vision algorithms (Measurements of vessels)
source quote (p.5)
“MPXA-2000 is developed based on Deep-learning (Classification and Segmentation of vessels) and Computer vision algorithms (Measurements of vessels) to analyze the images and provide quantification of the vessels in real-time.”
Adaptive (vs locked)FDA source did not state this
PCCPNo
Cybersecurity addressedYes
source quote (p.9)
“A cybersecurity verification testing was conducted to demonstrate the integrity, confidentiality, and availability of MPXA-2000 through testing for identified vulnerabilities of product.”

Validation studies (1)

Standalone

n=305 images

endpoints: vessel classification accuracy; vessel segmentation Dice similarity Coefficient (DSC)

standards: IEC 62304 Edition 1.1 2015-06 Medical device software - Software life cycle processes, General Principles of Software Validation (Jan 11, 2002), Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices: Guidance for Industry and FDA Staff (Nov 05, 2005), Content of Premarket Submissions for Management of Cybersecurity in Medical Devices, (Oct 2, 2014)

Reported performance (2 observations)

accuracyas written: “vessel classification accuracy”0.9934
source quote (p.8)
“The vessel classification accuracy: 0.9934”
diceas written: “vessel segmentation Dice similarity Coefficient (DSC)”0.92
source quote (p.8)
“The vessel segmentation Dice similarity Coefficient (DSC): 0.9200(Pass criteria of > 0.8)”

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/K222036