QIR Suite

K211611

CASIS Cardiac Simulation & Imaging Software · cleared 2022-09-30 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
“QIR Suite is intended to be used for viewing, post-processing, and quantitative evaluation of cardiovascular Magnetic Resonance (MR) images in a DICOM (Digital Imaging and Communication in Medicine) Standard format. The software has been validated for use on adult patients. QIR Suite comprises QIR-MR for analysis of MR images. QIR-MR is composed of a viewer and analysis modules, and uses user inputs, standard algorithms, and/or automated deep learning detection algorithms.”
Algorithmstandardized and deep-learning algorithms
source quote (p.6)
“QIR Suite is a software for quantitative analyses of cardiovascular magnetic resonance images in the DICOM format. Analyses are performed using standardized and deep-learning algorithms.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (2)

Retrospective clinical

sample size not stated

endpoints: correlation coefficient (R2); absolute mean difference

Standalone

sample size not stated

endpoints: Dice coefficient

Reported performance (3 observations)

diceas written: “Dice coefficient for AG algorithm”0.893
source quote (p.9)
“DICE measurements performed on large testing datasets gave a mean score of 0.893 and 0.888 for the AG and AG+ algorithms respectively, and 0.908 for the Fast algorithm.”
diceas written: “Dice coefficient for AG+ algorithm”0.888
source quote (p.9)
“DICE measurements performed on large testing datasets gave a mean score of 0.893 and 0.888 for the AG and AG+ algorithms respectively, and 0.908 for the Fast algorithm.”
diceas written: “Dice coefficient for Fast algorithm”0.908
source quote (p.9)
“DICE measurements performed on large testing datasets gave a mean score of 0.893 and 0.888 for the AG and AG+ algorithms respectively, and 0.908 for the Fast algorithm.”

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/K211611