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. It can be used for reconstruction of non-contrast enhanced MRI images acquired with 1.5T or 3T Siemens and GE scanners using Sagittal, Axial, or Coronal T1, T2, or FLAIR sequences. DeepFoqus-Accelerate is intended to be used on adult scans only and not intended for use on mobile devices.”

AlgorithmAI and machine learning models, combining various signal processing and machine learning techniques including several convolutional neural network (CNN) and U-net architecture 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.”

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 and (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: robustness of the AI-based DeepFoqus-Accelerate reconstruction against common MRI artifacts, ensuring that no additional reconstruction errors are introduced.

Retrospective clinical

sample size not stated

endpoints: determine whether the accelerated scans were equivalent 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

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).