Syngo Dynamics (Version VA40F)

K222428

Siemens Medical Solutions USA, Inc. · cleared 2022-11-14 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.5)
“syngo Dynamics is a software only medical device which is used with common IT hardware. Comparing with the predicate, the subject device is supplied with AI/ML algorithm that enable an optional semi-automated method (Auto EF) to calculate left ventricle ejection fraction using ultrasound images.”
AlgorithmAI/ML algorithm that enable an optional semi-automated method (Auto EF) to calculate left ventricle ejection fraction using ultrasound images.
source quote (p.10)
“Comparing with the predicate, the subject device is supplied with AI/ML algorithm that enable an optional semi-automated method (Auto EF) to calculate left ventricle ejection fraction using ultrasound images.”
Adaptive (vs locked)No
source quote (p.11)
“The subject device has an additional capability for semi-automated calculation of LV EF on images selected by the user and under full control of the user including possible manual intervention, performing equivalently to the manual measurement with no new concerns for the safety and effectiveness.”
PCCPNo
Cybersecurity addressedYes
source quote (p.11)
“Cybersecurity considerations related to syngo Dynamics are included within this submission. Siemens Healthineers conforms to cybersecurity requirements by implementing a means to prevent unauthorized access, modification, misuse, denial of use or unauthorized use of information stored, accessed or transferred from a medical device to an external recipient.”

Validation studies (1)

Retrospective clinical

n=150 cases · 3 site(s)

endpoints: Pearson's correlation coefficient r "≥" 0.800 between the biplane EF generated by Auto EF and the ground truth; total of 12 predetermined acceptance criteria

standards: NEMA PS 3.1 - 3.20 (2016), ISO IEC 10918-1 First edition 1994-02-15, IEC 62366-1 Edition 1.1 2020-06 CONSOLIDATED VERSION, ISO 14971 Third Edition 2019-12, IEEE Std 3333.2.1-2015, IEC 62304 Edition 1.1 2015-06 CONSOLIDATED VERSION, IEC TR 80001-2-2 Edition 1.0 2012-07, IEC 82304-1 Edition 1.0 2016-10

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/K222428