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)No

source quote (p.8)

“The Deep-learning algorithm used in MPXA-2000 was validated by the standalone performance test (Doc. No. TD-XA2-SPTR) using the method of comparing the analysis results obtained from MPXA-2000 with the ground truth annotated by experienced experts.”

PCCPFDA source did not state this

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)

Retrospective clinical

n=305 images

endpoints: vessel segmentation assessment; vessel classification assessment; ROI analysis assessment

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

recalls in product code, 24mo

MAUDE reports in code, 12mo

—

vs code's own 3-yr baseline

drift signals on this device

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). 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).