DeepFoqus (DeepFoqus-Accelerate)

K241982

Foqus Technologies Inc. · cleared 2025-04-04 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“DeepFoqus-Accelerate is a stand-alone software solution intended to be used for acceptance, enhancement, and transfer of brain MRI images in DICOM format.”
AlgorithmAI and machine learning models, combining various signal processing and machine learning techniques including several convolutional neural network (CNN) and U-net architecture models, using ensembles of deep learning models.
source quote (p.6)
“DeepFoqus-Accelerate uses AI and machine learning models to reconstruct MRI images from up to 4x accelerated MRI scans. The models combine various signal processing and machine learning techniques including several convolutional neural network (CNN) and U-net architecture models. The Subject implements an image reconstruction algorithm using ensembles of deep learning models. Ensembling aggregates several model architectures in order to produce a single reconstruction of accelerated image data.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.8)
“Penetration testing, secure code review, and dependency vulnerability scanning”

Validation studies (4)

Bench

sample size not stated

endpoints: Structural Similarity Index Measure (SSIM); Peak Signal to Noise Ratio (PSNR); Haar wavelet-based perceptual similarity index (HaarPSI)

Bench

sample size not stated

endpoints: geometric accuracy; intensity uniformity; percentage ghosting; signal-to-noise ratio; resolution; low-contrast detectability

Bench

sample size not stated

endpoints: quantitative (SSIM) and qualitative assessment of images which are expected to have artifacts

Reader study (MRMC)

sample size not stated

endpoints: equivalence to the ground truth

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/K241982