Ezra Flash

K242334

Ezra AI, Inc. · cleared 2025-01-02 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“Ezra Flash is a Software as a Medical Device (SaMD) consisting of a software algorithm that enhances images of the head, abdomen, and pelvis regions taken by MRI scanners.”
Algorithmconvolutional neural network-based filtering; cascade of filter banks, where thresholding and scaling operations are applied; single machine-learning model; image-guided optimization process
source quote (p.8)
“Ezra Flash software implements an image enhancement algorithm using a convolutional neural network-based filtering. Original images are enhanced by running through a cascade of filter banks, where thresholding and scaling operations are applied. These filters result in a single machine-learning model that reduces noise. A dedicated machine-learning model is used for the head and body. The parameters of the filters were obtained through an image-guided optimization process.”
Adaptive (vs locked)No
source quote (p.8)
“These filters result in a single machine-learning model that reduces noise. A dedicated machine-learning model is used for the head and body. The parameters of the filters were obtained through an image-guided optimization process.”
PCCPNo
Cybersecurity addressedYes
source quote (p.11)
“AAMI TIR57: 2016 - Principles For Medical Device Security - Risk Management”

Validation studies (1)

Bench

sample size not stated

endpoints: Signal-to-Noise Ratio (SNR); Contrast-to-Noise Ratio (CNR); Image Quality - Perceived Noise

standards: ISO 14971:2019 - Medical Devices - Application of risk management to medical devices, IEC 62304 Edition 1.1: 2015 - Medical device software - Software life cycle processes, NEMA PS 3.1-3.20 (2021e) - Digital Imaging and Communications in Medicine (DICOM) set, AAMI TIR57: 2016 - Principles For Medical Device Security - Risk Management

Reported performance (0 observations)

FDA source did not state a quantitative performance metric — non-reporting is itself the signal.

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/K242334