SomnoMetry

K221179

Neumetry Medical Inc · cleared 2022-09-21 · product code OLZ · Neurology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“The SomnoMetry is an Artificial Intelligent/Machine Learning (AI/ML)-enabled Software as a Medical Device (SaMD) that automatically scores sleep study results by analyzing polysomnography (PSG) signals recorded during sleep studies.”
AlgorithmArtificial Intelligent/Machine Learning (AI/ML)-enabled Software as a Medical Device (SaMD) that automatically scores sleep study results by analyzing polysomnography (PSG) signals recorded during sleep studies. The SomnoMetry SaMD employs a broad array of signal processing, data indexing, conventional machine learning and deep learning algorithms/approaches in PSG physiological signals to derive actionable clinical insights.
source quote (p.4)
“The SomnoMetry is an Artificial Intelligent/Machine Learning (AI/ML)-enabled Software as a Medical Device (SaMD) that automatically scores sleep study results by analyzing polysomnography (PSG) signals recorded during sleep studies. The SomnoMetry SaMD employs a broad array of signal processing, data indexing, conventional machine learning and deep learning algorithms/approaches in PSG physiological signals to derive actionable clinical insights.”
Adaptive (vs locked)No
source quote (p.4)
“All automatically scored events are subject to verification by a qualified clinician. The AI/ML algorithms do not introduce any new risks or unexpected results.”
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.7)
“A cybersecurity and data security testing were conducted to verify that data and patient protected health information security measures are thoroughly included in the design of the software. Web-based software operates in the cloud with Windows, Mac OS, or Linux User authentication with strong password, authorization, end to end SSL encryption, access controls, checksum, network and database controls, intrusion prevention system, and anonymization.”

Validation studies (1)

Retrospective clinical

n=201 patients · 2 site(s)

endpoints: device performance for sleep staging scoring must be validated.; device performance for diagnosing sleep apnea must be validated.

standards: IEC 62304:2006/A1:2015, Medical Device Software – Software life cycle processes, ISO 14971:2007, Medical Devices – Application of Risk Management to Medical Devices

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
2
MAUDE reports in code, 12mo
+20%
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 Neurology 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/K221179