NvisionVLE Imaging System, NvisionVLE Optical Probe, NvisionVLE Inflation System

K182616

NinePoint Medical, Inc. · cleared 2018-11-02 · product code NQQ · Radiology

Premarket evidence — what FDA accepted

Device typehardware with ml
source quote (p.5)
The NinePoint Medical NvisionVLE® Imaging System is a high-resolution volumetric imaging system based on optical coherence tomography (OCT). In an analogous fashion to ultrasound imagery, OCT images are formed from the time delay and magnitude of the signal reflected from the tissue of interest. The NvisionVLE Imaging System employs an advanced form of OCT known as swept-source OCT (SS-OCT), or Optical Frequency Domain Imaging (OFDI), in combination with a scanning optical probe to acquire high-resolution, cross-sectional, real-time imagery of tissue called Volumetric Laser Endomicroscopy (VLE).
Algorithmartificial intelligence machine learning technique known as deep learning, using an artificial neural network
source quote (p.6)
The segmentation algorithm was developed using an artificial intelligence machine learning technique known as deep learning. Here, an artificial neural network was trained with manually labelled examples of each feature and then locked for real-time inference on new image data acquired by the device.
Adaptive (vs locked)No
source quote (p.6)
Here, an artificial neural network was trained with manually labelled examples of each feature and then locked for real-time inference on new image data acquired by the device.
PCCPFDA source did not state this
Cybersecurity addressedFDA source did not state this

Validation studies (1)

Retrospective clinical

n=473 other · 18 site(s)

endpoints: evaluate the ability of the software to detect each IVE feature including tissue surface, regions with layering and absence of layering and hypo-reflective structures; target true positive and true negative detection fractions

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
13
MAUDE reports in code, 12mo
+179%
vs code's own 3-yr baseline
1
drift signals on this device
  • adverse_event_inflection

    MAUDE adverse-event reports for product code NQQ: 13 in the 12 months ending 2026-06, vs a 4.7/12mo average over the prior 3 windows (+179%). Code-level count — reports are not attributed to this specific device.

    first seen 2026-07-08 · openFDA /device/event.json count=date_received product_code=NQQ

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). 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).

RIGOR™ Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: radar.healthai.com/precedent/device/K182616