SubtleHD (1.x)

K243250

Subtle Medical, Inc. · cleared 2025-02-12 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“SubtleHD is Software as a Medical Device (SaMD) consisting of a software algorithm that enhances images taken by MRI scanners.”
Algorithmconvolutional neural network based filtering
source quote (p.6)
“SubtleHD 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. A single neural network is trained for adaptive noise reduction and sharpness increase. The parameters within the neural network were obtained through an image-guided optimization process. Additional nonlocal mean based denoising and unsharp masking based sharpening filters are applied to the deep learning processed image.”
Adaptive (vs locked)No
source quote (p.9)
“The algorithm will be locked with fixed model parameters prior to release.”
PCCPYes
source quote (p.9)
“SubtleHD has a Predetermined Change Control Plan (PCCP), which details planned device modifications, the associated methodology to develop, validate, and implement those modifications, and an assessment of the impact of those modifications.”
Cybersecurity addressedFDA source did not state this

Validation studies (4)

Bench

sample size not stated

Bench

n=471 cases

endpoints: L1 loss; SSIM; PSNR

Retrospective clinical

sample size not stated

endpoints: Denoising (SNR); Sharpness (Image Intensity Change); Sharpness (Image Intensity Change for Brains); Sharpness and Over Smoothing (Gradient Entropy)

Reader study (MRMC)

n=410 images

endpoints: Signal-to-Noise Ratio; Overall Image Quality / Diagnostic Confidence; Visibility of Small Structures; Artifact Introduction

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/K243250