AI-Rad Companion Brain MR

K253057

Siemens Healthcare GmbH · cleared 2026-01-22 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“AI-Rad Companion Brain MR is a post-processing image analysis software that assists clinicians in viewing, analyzing, and evaluating MR brain images.”
AlgorithmTwo distinct and independent algorithms for Brain Morphometry analysis and White Matter Hyperintensities (WMH) segmentation. Brain morphometry quantifies volumetric properties of brain structures based on T1 MPRAGE datasets and compares normalized values against age-matched mean and standard deviations from a healthy reference population. WMH feature quantifies white matter hyperintensities on T1 MPRAGE and T2 weighted FLAIR datasets.
source quote (p.7)
“AI-Rad Companion Brain MR runs two distinct and independent algorithms for Brain Morphometry analysis and White Matter Hyperintensities (WMH) segmentation, respectively. In overall, comprises four main algorithmic features: Brain Morphometry, Brain Morphometry follow-up, White Matter Hyperintensities (WMH), White Matter Hyperintensities (WMH) follow-up. Just as in the predicate, the brain morphometry feature of AI-Rad Companion Brain MR addresses the automatic quantification and visual assessment of the volumetric properties of various brain structures based on T1 MPRAGE datasets. From a predefined list of brain structures (e.g. Hippocampus, Caudate, Left Frontal Gray Matter, etc.) volumetric properties are calculated as absolute and normalized volumes with respect to the total intracranial volume. The normalized values are compared against age-matched mean and standard deviations obtained from a population of healthy reference subjects. The deviation from this reference population can be visualized as 3D overlay map or out-of-range flag next to the quantitative values. Additionally, identical to the predicate, the white matter hyperintensities feature addresses the automatic quantification and visual assessment of white matter hyperintensities on the basis of T1 MPRAGE and T2 weighted FLAIR datasets.”
Adaptive (vs locked)No
source quote (p.16)
“WMH follow-up algorithm does not include any machine learning/ deep learning component.”
PCCPNo
Cybersecurity addressedYes
source quote (p.14)
“Siemens Healthineers adheres to the cybersecurity recommendations as defined the FDA Guidance "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions” (September 2023) by implementing a process of preventing unauthorized access, modifications, misuse or denial of use, or the unauthorized use of information that is stored, accessed, or transferred from a medical device to an external recipient.”

Validation studies (2)

Retrospective clinical

n=100 patients

endpoints: Pearson correlation coefficient between the WMH volumes estimated by our software and ground truth annotation; interclass correlation coefficient between the WMH volumes estimated by our software and ground truth annotation; segmentation accuracy of the WMH (Dice score); F1-score of WMH detection

Retrospective clinical

n=165 patients

endpoints: Pearson correlation coefficient between the new or enlarged WMH volumes estimated by our software and ground truth annotation; segmentation accuracy of the new or enlarged WMH (Dice score); F1-score of new or enlarged WMH detection

Reported performance (5 observations)

diceas written: “Dice score (WMH segmentation accuracy)”0.6
source quote (p.14)
“The segmentation accuracy of the WMH reaches a Dice score of 0.60.”
f1as written: “F1-score (WMH detection)”0.67
source quote (p.14)
“The F1-score of WMH detection is 0.67.”
diceas written: “Dice (Mean)”0.6CI [0.57, 0.63]
source quote (p.15)
“Mean 0.60 ... 95% CI [0.57, 0.63]”
diceas written: “Dice score (new or enlarged WMH segmentation accuracy)”0.59
source quote (p.15)
“The segmentation accuracy of the new or enlarged WMH reaches an average Dice score of 0.59.”
f1as written: “F1-score (new or enlarged WMH detection)”0.71
source quote (p.15)
“The average F1-score of new or enlarged WMH detection is 0.71.”

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/K253057