uMR Ultra

K243547

Shanghai United Imaging Healthcare Co., Ltd. · cleared 2025-07-17 · product code LNH · Radiology

Premarket evidence — what FDA accepted

Device typehardware with ml
source quote (p.4)
“The uMR Ultra system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces sagittal, transverse, coronal, and oblique cross sectional images, and spectroscopic images, and that display internal anatomical structure and/or function of the head, body and extremities. ... DeepRecon is a deep-learning based image processing algorithm for intelligent image de-noising and K-space-interpolation based image super-resolution.”
AlgorithmDeep-learning based image processing algorithm for intelligent image de-noising and K-space-interpolation based image super-resolution.
source quote (p.20)
“DeepRecon is a deep-learning based image processing algorithm for intelligent image de-noising and K-space-interpolation based image super-resolution.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.16)
“Content of Premarket Submissions for Management of Cybersecurity in Medical Devices”

Validation studies (17)

Retrospective clinical

n=25 patients

endpoints: Image SNR; Image Resolution; Image Contrast; Image Uniformity; Structure Measurement; clinical diagnosis quality

standards: NEMA MS 6-2008(R2004)

Retrospective clinical

n=25 patients

endpoints: Image SNR; Image uniformity; Image contrast; Structure measurement; clinical diagnosis quality

Retrospective clinical

n=116 patients

endpoints: auto position success rate

Retrospective clinical

n=60 patients

endpoints: quantification test (MAE, PSNR, SSIM); local structure measurement; temporal image performance test (motion-time curves, Bland-Altman analysis)

Retrospective clinical

n=24 patients

endpoints: PSNR; SSIM; Local Structural Measurements Test; image quality for clinical diagnosis

Retrospective clinical

n=15 patients

endpoints: detection accuracy; PSNR of spark-corrected images; image quality improvement

Retrospective clinical

n=80 patients

endpoints: success rate

Retrospective clinical

n=65 patients

endpoints: pass rate

Retrospective clinical

n=25 patients

endpoints: satisfied and acceptable ratio

Retrospective clinical

n=27 patients

endpoints: average frame difference; maximum frame difference

Retrospective clinical

n=60 patients

endpoints: Dice coefficient

Retrospective clinical

n=33 patients

endpoints: Dice coefficient

Retrospective clinical

n=56 patients

endpoints: average error between phase indices

Retrospective clinical

n=28 patients

endpoints: satisfaction rate

Retrospective clinical

n=63 patients

endpoints: PH5; PH15; MEAN_H

Retrospective clinical

n=63 patients

endpoints: PW10; PW20; MEAN_W

Retrospective clinical

n=20 patients

endpoints: success rate

Reported performance (3 observations)

accuracyas written: “Spark Detection Accuracy”0.94
source quote (p.28)
“The average detection accuracy is 94%.”
diceas written: “Cardiac Perfusion Dice Coefficient”0.92
source quote (p.37)
“The average Dice coefficient of the left ventricular myocardium after motion correction is 0.92, which is greater than 0.87.”
diceas written: “Cardiac Dark Blood Dice Coefficient”0.96
source quote (p.38)
“The average Dice coefficient of the left ventricular myocardium after motion correction is 0.96, which is greater than 0.87.”

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

100
recalls in product code, 24mo
510
MAUDE reports in code, 12mo
+5%
vs code's own 3-yr baseline
3
drift signals on this device
  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K252371 (decision 2025-09-25) from Shanghai United Imaging Healthcare Co., Ltd. for a matching device line ("uMR 680") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K252371

  • recall_reason_pattern

    Software/algorithm-related recall in product code LNH (Philips North America, initiated 2026-04-14): "The potential for stiffness value errors when a specific range of image reconstruction parameters is used in combination with Resoundant's algorithm, leading to the reconstruction " Recalling firm is another firm in the same product code.

    first seen 2026-07-08 · recall res_event_number:98779

  • recall_reason_pattern

    Software/algorithm-related recall in product code LNH (Philips North America, initiated 2025-12-03): "The potential for stiffness value errors when viewing exported MR Elastography (MRE) stiffness maps to viewer Picture Archiving and Communication System (PACS)." Recalling firm is another firm in the same product code.

    first seen 2026-07-08 · recall res_event_number:98111

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/K243547