ENT EM

K223734

Brainlab AG · cleared 2023-04-27 · product code PGW · Neurology

Premarket evidence — what FDA accepted

Device typehardware with ml
source quote (p.5)
“The Subject Device ENT EM is an image guided planning and navigation system to enable navigated surgery during ENT procedures. It offers guidance for setting up the EM equipment, different patient image registration methods and instrument selection and calibration to allow surgical navigation by using electromagnetic tracking (EM) technology. The device provides different workflows guiding the user through preoperative and intraoperative steps. [...] With this submission, an already existing feature is now performed introducing a new algorithm using artificial intelligence and machine learning (AI/ML).”
AlgorithmConvolutional Neuronal Network (CNN) developed using a Supervised Learning approach
source quote (p.5)
“The Al/ML algorithm is a Convolutional Neuronal Network (CNN) developed using a Supervised Learning approach.”
Adaptive (vs locked)No
source quote (p.5)
“This is a static algorithm (locked).”
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.7)
“(with minor modifications to e.g. strengthen cybersecurity)”

Validation studies (4)

Bench

sample size not stated

endpoints: Mean Positional Error of the placed instrument's tip "," 2 mm; Mean Angular Error of the placed instrument's axis "," 2°

Standalone

sample size not stated

endpoints: summative usability evaluation in a simulated clinical environment

Bench

sample size not stated

endpoints: Compliance to electrical safety, RFID and EMC standards

standards: IEC 60601-1, AIM 7351731, IEC 60601-1-2

Bench

sample size not stated

endpoints: Biocompatibility assessment considering different end points; Cleaning and disinfection evaluation/reprocessing validation; Mechanical properties of instruments evaluation

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

2
recalls in product code, 24mo
116
MAUDE reports in code, 12mo
-52%
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 Neurology 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/K223734