UltraSight Guidance

K251416

Ultrasight , Ltd. · cleared 2025-12-17 · product code QJU · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“UltraSight Guidance is a software application based on machine learning that uses artificial intelligence (Al) to provide dynamic real-time guidance on the position and orientation of the transducer to help non-expert users acquire diagnostic-quality tomographic views of the heart.”
Algorithmsoftware application based on machine learning that uses artificial intelligence (AI) and MLM based algorithms
source quote (p.5)
“UltraSight Guidance is a software application based on machine learning that uses artificial intelligence (Al) to provide dynamic real-time guidance on the position and orientation of the transducer to help non-expert users acquire diagnostic-quality tomographic views of the heart. The system provides guidance for ten standard cardiac views. ... Both systems apply MLM based algorithms to provide real-time guidance on how to position and manipulate the transducer on a patient's body.”
Adaptive (vs locked)No
PCCPYes
source quote (p.1)
“FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP).”
Cybersecurity addressedYes
source quote (p.10)
“The device underwent comprehensive software validation and cybersecurity testing in accordance with the FDA's Guidance "Content of Premarket Submissions for Device Software Functions" and "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions". These evaluations ensured the software meets its intended use, functions reliably under expected conditions, and incorporates appropriate risk-based cybersecurity controls. The submission includes documentation of threat modeling, vulnerability testing, and secure design practices to support the safety and effectiveness of the device in today's evolving cybersecurity landscape.”

Validation studies (2)

Retrospective clinical

n=134 patients · 2 site(s)

endpoints: classification performance between “diagnosable” and “non diagnosable" clips of each view

Retrospective clinical

n=134 patients · 2 site(s)

endpoints: frame level accuracy of each guidance cue prediction

Reported performance (1 observation)

aurocas written: “Mean AUC (Probe Guidance)”stated without valueCI 95% CI showing good classification performance
source quote (p.10)
“The mean AUC was within the acceptance criteria of AUC>0.8 with 95% CI showing good classification performance.”

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/K251416