SwiftMR

K253775

Airs Medical, Inc. · cleared 2026-03-26 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd

source quote (p.5)

“SwiftMR is software used as a Medical Device (SaMD) consisting of a software algorithm that enhances images taken by MRI scanners.”

Algorithmdeep learning algorithm

source quote (p.5)

“The deep learning algorithm produces enhanced images as outputs with reduced noise and increased sharpness in DICOM format.”

Adaptive (vs locked)FDA source did not state this

PCCPFDA source did not state this

Cybersecurity addressedFDA source did not state this

Validation studies (1)

Retrospective clinical

sample size not stated

endpoints: average signal-to-noise ratio (SNR) of the SwiftMR-processed image series is increased by 40% or more for at least 90% of the dataset for level 1 with an incremental 1% increase per each level; FWHM of a selected region of interest (ROI) is decreased by 0.13% (deep learning model), 0.43% (filter level 1), 1.7% (filter level 2), 2.3% (filter level 3), 3.6% (filter level 4), 4.5% (filter level 5) or more for at least 90% of the dataset

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

predicate_cohort_contagion
K253775 shares predicate K230854 with K251901 (Magnifico Open (100009900)), whose product code LNH recorded 100 recall event(s) in the trailing 24 months.
first seen 2026-07-08 · predicate:K230854 sibling:K251901
predicate_cohort_contagion
K253775 shares predicate K230854 with K260746 (S-scan Open (100001800)), whose product code LNH recorded 100 recall event(s) in the trailing 24 months.
first seen 2026-07-08 · predicate:K230854 sibling:K260746

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).