VELYS™ Hip Navigation

K253551

Depuy Ireland UC · cleared 2026-03-06 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“VELYST™ Hip Navigation (VHN) is a Software as a Medical Device that provides the clinician with intra-operative measurements and visuals of acetabular cup orientation, femoral component leg length and offset calculations, and implant constructs based on user-defined, but machine learning (ML) default-positioned, bony-anatomy landmark points.”
Algorithmmachine learning (ML) default-positioned, bony-anatomy landmark points. VHN includes a machine learning model that places the default position of the landmark based on the output of the model; the user has full control to manipulate the landmark positions after placement. The model inputs the x-ray or fluoroscopy images and outputs a default location for the landmark annotation tool. This machine learning model is Human-in-the-Loop, as the user is expected to position the annotation as they see fit.
source quote (p.6)
“VELYST™ Hip Navigation (VHN) is a Software as a Medical Device that provides the clinician with intra-operative measurements and visuals of acetabular cup orientation, femoral component leg length and offset calculations, and implant constructs based on user-defined, but machine learning (ML) default-positioned, bony-anatomy landmark points. VHN includes a machine learning model that places the default position of the landmark based on the output of the model; the user has full control to manipulate the landmark positions after placement. The model inputs the x-ray or fluoroscopy images and outputs a default location for the landmark annotation tool. This machine learning model is Human-in-the-Loop, as the user is expected to position the annotation as they see fit.”
Adaptive (vs locked)No
source quote (p.6)
“This machine learning model is Human-in-the-Loop, as the user is expected to position the annotation as they see fit.”
PCCPNo
Cybersecurity addressedNo

Validation studies (1)

Bench

n=18,550 images

endpoints: leg length; femoral offset; total offset; cup inclination; cup anteversion

standards: ISO 13485 clause 7.3, ISO 13485 clause 8.3, ISO 13485 clause 8.5, 21 CFR Part 820

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

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/K253551