X9 Ultrasound System

K251673

X9, Inc. · cleared 2025-10-17 · product code SGH · Radiology

Premarket evidence — what FDA accepted

Device typehardware with ml
source quote (p.5)
“The X9 Ultrasound System consists of a Handpiece and Software. The Handpiece is a handheld device with an ultrasound transducer and is attached via a cable to a user-supplied Computer. The Handpiece contains a physical button for the user to activate/deactivate the system and an alignment marking to assist the user with positioning the Handpiece over the vessel. The software, which runs on a standard operating system platform installed on the Computer, provides the Graphical User Interface (GUI). The GUI will indicate when the Handpiece is aligned with the AVF/G. The information given from the system is intended to guide the user so that they can efficiently proceed with the standard of care assessment prior to cannulation. A Machine Learning Model Validation was performed to confirm that the ML model correctly determined if an access vessel was visible and if the average lateral error between the ML Model and the access vessel True Location was acceptable.”
AlgorithmMachine Learning Model to determine if an access vessel is visible and calculate lateral error.
source quote (p.7)
“A Machine Learning Model Validation was performed to confirm that the ML model correctly determined if an access vessel was visible and if the average lateral error between the ML Model and the access vessel True Location was acceptable.”
Adaptive (vs locked)FDA source did not state this
PCCPNo
Cybersecurity addressedYes
source quote (p.7)
“Cybersecurity Testing”

Validation studies (1)

Retrospective clinical

n=63 patients

endpoints: Access Presence Sensitivity percentage; average lateral error between the ML Model and the access vessel True Location; average Dice Similarity Coefficient (DSC)

Reported performance (2 observations)

sensitivity94.5
source quote (p.8)
“The calculated Access Presence Sensitivity was 94.5%, which exceeded the acceptance criteria of greater than or equal to 75%.”
diceas written: “average Dice Similarity Coefficient (DSC)”81.3
source quote (p.8)
“The average Dice Similarity Coefficient (DSC) across all reviewers was 81.3%, which meets the acceptance criterion of 75% minimum.”

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
0
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/K251673