Belun Sleep System BLS-100

K222579

Belun Technology Company Limited · cleared 2023-02-23 · product code MNR · Anesthesiology

Premarket evidence — what FDA accepted

Device typehardware with ml
source quote (p.4)
“The Belun Sleep System BLS-100 comprises a sensor that is worn on the proximal phalanx of index finger (Belun Ring) over-night whilst the subject is sleeping and a stand-alone analysis software (Belun Sleep Al).”
Algorithmdeep-learning algorithms
source quote (p.8)
“The clinical evaluation has confirmed that the Belun Sleep System deep-learning algorithms calculating the Belun Apnea Hypopnea Index (bAHI) and Belun Sleep Stage (bSTAGES) generate comparable output to human manual scoring of an Apnea Hypopnea Index (AHI) from Polysomnography (PSG) studies, using American Academy of Sleep Medicine (AASM) scoring guidelines for adult patients, with accuracy, sensitivity and specificity similar to the predicate and reference devices.”
Adaptive (vs locked)FDA source did not state this
PCCPNo
Cybersecurity addressedYes
source quote (p.10)
“FDA Guidance for the Content of Premarket Submissions for Management of Cybersecurity in Medical Devices”

Validation studies (2)

Bench

sample size not stated

standards: IEC 60601-1-2:2014, IEC 60601-1-11:2015, IEC 60601-1:2005 + a1:2012, IEC 62133:2012, ISO 80601-2-61:2017, ISO 10993-1:2018, ISO 10993-5:2009, ISO 10993-10:2010, IEC 62366-1:2015

Retrospective clinical

n=106 patients · 1 site(s)

endpoints: accuracy; sensitivity; specificity of the Apnea-Hypopnea Index (AHI); sleep staging (wake, Rapid Eye Movement (REM) and Non-Rapid Eye Movement (NREM))

Reported performance (15 observations)

sensitivity0.898
source quote (p.11)
“The accuracy, sensitivity and specificity at AHI cutoff 15 and 30 are summarized in the table below. Cutoff 15: Sensitivity 0.898”
specificity0.86
source quote (p.11)
“The accuracy, sensitivity and specificity at AHI cutoff 15 and 30 are summarized in the table below. Cutoff 15: Specificity 0.860”
accuracyas written: “Accuracy (AHI cutoff 15)”0.877
source quote (p.11)
“The accuracy, sensitivity and specificity at AHI cutoff 15 and 30 are summarized in the table below. Cutoff 15: Accuracy 0.877”
accuracyas written: “Accuracy (AHI cutoff 30)”0.925
source quote (p.11)
“The accuracy, sensitivity and specificity at AHI cutoff 15 and 30 are summarized in the table below. Cutoff 30: Accuracy 0.925”
sensitivityas written: “Sensitivity (AHI cutoff 30)”0.84
source quote (p.11)
“The accuracy, sensitivity and specificity at AHI cutoff 15 and 30 are summarized in the table below. Cutoff 30: Sensitivity 0.840”
specificityas written: “Specificity (AHI cutoff 30)”0.951
source quote (p.11)
“The accuracy, sensitivity and specificity at AHI cutoff 15 and 30 are summarized in the table below. Cutoff 30: Specificity 0.951”
accuracyas written: “Accuracy (Wake)”0.885
source quote (p.11)
“The accuracy, sensitivity, and specificity of 3-categorization sleep stages [wake, Rapid Eye Movement (REM) and Non-Rapid Eye Movement (NREM)] are summarized in the table below. Class Wake: Accuracy 0.885”
sensitivityas written: “Sensitivity (Wake)”0.604
source quote (p.11)
“The accuracy, sensitivity, and specificity of 3-categorization sleep stages [wake, Rapid Eye Movement (REM) and Non-Rapid Eye Movement (NREM)] are summarized in the table below. Class Wake: Sensitivity 0.604”
specificityas written: “Specificity (Wake)”0.961
source quote (p.11)
“The accuracy, sensitivity, and specificity of 3-categorization sleep stages [wake, Rapid Eye Movement (REM) and Non-Rapid Eye Movement (NREM)] are summarized in the table below. Class Wake: Specificity 0.961”
accuracyas written: “Accuracy (REM)”0.908
source quote (p.11)
“The accuracy, sensitivity, and specificity of 3-categorization sleep stages [wake, Rapid Eye Movement (REM) and Non-Rapid Eye Movement (NREM)] are summarized in the table below. Class REM: Accuracy 0.908”
sensitivityas written: “Sensitivity (REM)”0.712
source quote (p.11)
“The accuracy, sensitivity, and specificity of 3-categorization sleep stages [wake, Rapid Eye Movement (REM) and Non-Rapid Eye Movement (NREM)] are summarized in the table below. Class REM: Sensitivity 0.712”
specificityas written: “Specificity (REM)”0.944
source quote (p.11)
“The accuracy, sensitivity, and specificity of 3-categorization sleep stages [wake, Rapid Eye Movement (REM) and Non-Rapid Eye Movement (NREM)] are summarized in the table below. Class REM: Specificity 0.944”
accuracyas written: “Accuracy (NREM)”0.827
source quote (p.11)
“The accuracy, sensitivity, and specificity of 3-categorization sleep stages [wake, Rapid Eye Movement (REM) and Non-Rapid Eye Movement (NREM)] are summarized in the table below. Class NREM: Accuracy 0.827”
sensitivityas written: “Sensitivity (NREM)”0.904
source quote (p.11)
“The accuracy, sensitivity, and specificity of 3-categorization sleep stages [wake, Rapid Eye Movement (REM) and Non-Rapid Eye Movement (NREM)] are summarized in the table below. Class NREM: Sensitivity 0.904”
specificityas written: “Specificity (NREM)”0.695
source quote (p.11)
“The accuracy, sensitivity, and specificity of 3-categorization sleep stages [wake, Rapid Eye Movement (REM) and Non-Rapid Eye Movement (NREM)] are summarized in the table below. Class NREM: Specificity 0.695”

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
37
MAUDE reports in code, 12mo
+171%
vs code's own 3-yr baseline
1
drift signals on this device
  • adverse_event_inflection

    MAUDE adverse-event reports for product code MNR: 37 in the 12 months ending 2026-06, vs a 13.7/12mo average over the prior 3 windows (+171%). Code-level count — reports are not attributed to this specific device.

    first seen 2026-07-08 · openFDA /device/event.json count=date_received product_code=MNR

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 Anesthesiology 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/K222579