Oxevision Sleep Device

K233618

Oxehealth Limited · cleared 2024-04-03 · product code LEL · Neurology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“Oxevision Sleep is a software-only medical device (SaMD) that provides non-contact sleep assessment in the inpatient setting based on the analysis of patient movement, activity and physiological sign data derived from video, without the need for contact devices to be attached to the patient or bed.”
AlgorithmProprietary software-controlled algorithms are used to derive patient movement, activity and physiological sign data and then to obtain information on bed occupancy and sleep state from the analysis of this data.
source quote (p.6)
“Proprietary software-controlled algorithms are used to derive patient movement, activity and physiological sign data and then to obtain information on bed occupancy and sleep state from the analysis of this data.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.10)
“The firm has also performed penetration and vulnerability testing in line with FDA's guidance on cybersecurity.”

Validation studies (1)

Retrospective clinical

n=60 patients

endpoints: The accuracy of periods of bed occupancy detected using Oxevision Sleep Device algorithms measured against periods of human-labeled bed occupancy is not inferior to 95%; The agreement between Oxevision Sleep Device algorithms in sleep/wake classification measured against polysomnographic reference standard during periods of bed occupancy detected using Oxevision Sleep Device algorithms is not inferior to Agreement = 82%, Positive agreement = 88%, and Negative Agreement = 55%

standards: ISO 14971:2019 + A11:2021 - Application of risk management to medical devices, AAMI BS 34971:2022 - Application of risk management to Artificial Intelligence and Machine Learning Devices, IEC 62304:2006 + A1:2015 - Software life cycle processes, IEC 82304-1:2016 - Health software, IEC 80001-1:2021 - Application of risk management for IT-networks incorporating medical devices Part 1: Safety, effectiveness and security in the implementation and use of connected medical devices or connected health software, IEC 81001-5-1:2021 - Health software and health IT systems safety, effectiveness and security Part 5-1: Security Activities in the product life cycle, IEC/TR 80002-1:2009 - Medical device software Part 1: Guidance on the application of ISO 14971 to medical device software, IEC 62366-1:2015 +A1:2020 - Application of risk management to medical devices, ISO 15223-1:2021 - Medical devices Symbols to be used with information to be supplied by the manufacturer, ISO 20417:2021 Symbols to be used with information to be supplied by the manufacturer

Reported performance (3 observations)

sensitivity0.88CI (92.3%; 95.6%)
source quote (p.10)
“Positive agreement = 88%... (92.3%; 95.6%)”
specificity0.55CI (74.3%; 83.5%)
source quote (p.10)
“Negative Agreement = 55%... (74.3%; 83.5%)”
accuracyas written: “Bed State Accuracy”0.95CI (99.0%; 99.7%)
source quote (p.10)
“The accuracy of periods of bed occupancy detected using Oxevision Sleep Device algorithms measured against periods of human-labeled bed occupancy is not inferior to 95%... (99.0%; 99.7%)”

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 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/K233618