Brainlab Elements (7.0); Brainlab Elements Image Fusion (5.0); Brainlab Elements Image Fusion Angio (1.0); Brainlab Elements Contouring (5.0); Brainlab Elements Fibertracking (3.0); Brainlab Elements BOLD MRI Mapping (1.0)

K243633

Brainlab AG · cleared 2025-06-13 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“The Brainlab Elements are applications and background services for processing of medical images including functionalities such as data transfer, image co-registration, image segmentation, contouring and other image processing.”
Algorithmmachine learning algorithm; atlas-based automatic segmentation; deterministic tracking algorithm; probabilistic tracking algorithm; constrained spherical deconvolution tracking
source quote (p.13)
“Cranial tumors are auto-segmented as 3D objects in image sets with supported modality (MR-t1 contrast enhanced) by means of a machine learning algorithm. Anomaly detection is either the result of the atlas-based automatic segmentation of the brain or, on platforms with appropriate GPUs, the result of a machine learning algorithm. Deterministic Tracking Algorithm based on Combined Single/Dual Tensor Tracking Probabilistic tracking algorithm Constrained Spherical Deconvolution tracking”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (3)

Retrospective clinical

n=412 patients

endpoints: Dice ", "0.7; Recall ", "0.8; Precision ", "0.8 for the lower bound of the respective 95 % confidence intervals

Retrospective clinical

sample size not stated

endpoints: validate that Elements Virtual iMRI Cranial can be applied to cranial MR and intraoperative MR/CT/US image data related to image guided surgery approaches to compensate for surgery-related brain shift during resection

Bench

sample size not stated

endpoints: validate if Fibertracking allows to visualize cranial white matter structures such as motoric, language and visual tracts based on state of the art approaches for Fibertracking as Constrained Spherical Deconvolution (CSD) and probabilistic tracking

Reported performance (15 observations)

sensitivity0.85
source quote (p.22)
“Recall 0.85”
diceas written: “Dice”0.75
source quote (p.22)
“Dice 0.75”
ppvas written: “Precision”0.86
source quote (p.22)
“Precision 0.86”
diceas written: “Dice (Metastases to the CNS)”0.74
source quote (p.22)
“Dice 0.74”
ppvas written: “Precision (Metastases to the CNS)”0.85
source quote (p.22)
“Precision 0.85”
sensitivityas written: “Recall (Metastases to the CNS)”0.84
source quote (p.22)
“Recall 0.84”
diceas written: “Dice (Meningiomas)”0.76
source quote (p.23)
“Dice 0.76”
ppvas written: “Precision (Meningiomas)”0.89
source quote (p.23)
“Precision 0.89”
sensitivityas written: “Recall (Meningiomas)”0.9
source quote (p.23)
“Recall 0.90”
diceas written: “Dice (Cranial and paraspinal nerve tumors)”0.89
source quote (p.23)
“Dice 0.89”
ppvas written: “Precision (Cranial and paraspinal nerve tumors)”0.97
source quote (p.23)
“Precision 0.97”
sensitivityas written: “Recall (Cranial and paraspinal nerve tumors)”0.97
source quote (p.23)
“Recall 0.97”
diceas written: “Dice (Gliomas and glio-/neuronal tumors)”0.81
source quote (p.23)
“Dice 0.81”
ppvas written: “Precision (Gliomas and glio-/neuronal tumors)”0.95
source quote (p.23)
“Precision 0.95”
sensitivityas written: “Recall (Gliomas and glio-/neuronal tumors)”0.85
source quote (p.23)
“Recall 0.85”

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/K243633