PIUR tUS inside

K250484

PIUR Imaging GmbH · cleared 2025-06-30 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typehardware with ml
source quote (p.5)
“PIUR tUS inside is a medical device which enhances standard ultrasound devices with a three-dimensional (3D) tomographic imaging method for a 3D analysis of ultrasound volumes. With PIUR tUS inside, examining physicians can make diagnostic decisions based on standard 2D as well as 3D image data integrated in an ultrasound device environment. This 3D data provides information which previously could have only been generated using other 3D imaging technologies like CT or MRI. The device performs 2D to 3D reconstruction to generate volumetric data of a thyroid. User-selected computer vision and machine learning algorithm suggested volumes of thyroid lobe and nodules are visualized to be confirmed by user. Based on that segmentation, quantification of nodules for sonographic characteristics (hyperechoic foci, echogenicity, texture, margin, orientation and anechoic areas) is being performed and visualized.”
Algorithmcomputer vision and machine learning algorithm
source quote (p.10)
“User-selected computer vision and machine learning algorithm suggested volumes of thyroid lobe and nodules are visualized to be confirmed by user.”
Adaptive (vs locked)No
PCCPNo
Cybersecurity addressedYes
source quote (p.8)
“PIUR tUS inside has been designed to meet cybersecurity requirements using design vulnerability assessments utilizing the Common Vulnerability Scoring System (CVSS), providing open source and 3rd party libraries in the SBOM's and performing gray box penetration testing.”

Validation studies (1)

Standalone

sample size not stated

endpoints: suggested ROIs of user-selected nodules

standards: ISO 13485:2016, ISO 14971:2019, IEC 62366-1:2015, IEC 62304:2015, IEC 82304-1:2016, EN 301 489-1 V2.2.3 (2019-11), EN 301 489-17 V3.2.4:2020, IEC 60601-1:2013, IEC 60601-1-2:2014, IEC 60601-2-37:2016, ISO 15223-1:2021, IEC 60417:2002, NEMA PS 3.1-3.20:2023

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/K250484