Sleep Apnea Notification Feature (SANF)

K240929

Apple Inc. · cleared 2024-09-13 · product code QZW · Anesthesiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“The Sleep Apnea Notification Feature (SANF) is a software-only mobile medical application that analyzes Apple Watch sensor data to identify patterns of breathing disturbances suggestive of moderate-to-severe sleep apnea and provides a notification to the user.”
Algorithmdeep learning algorithm
source quote (p.9)
“SANF includes a deep learning algorithm to identify breathing disturbances using accelerometer sensor data from Apple Watch.”
Adaptive (vs locked)No
source quote (p.12)
“The PCCP does not include provisions for implementation of adaptive algorithms that will continuously learn in the field. All algorithm modifications will be trained, tuned, and locked prior to release of the software to the field.”
PCCPYes
source quote (p.1)
“FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP) titled “SLEEP APNEA NOTIFICATION FEATURE (SANF) PREDETERMINED CHANGE”
Cybersecurity addressedYes
source quote (p.10)
“Apple approach to cybersecurity aligns with FDA's 2023 Guidance, " Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions." The device also conforms to the cybersecurity requirements identified in Section 524B to the FD&C Act.”

Validation studies (1)

Prospective clinical

n=1,499 patients

endpoints: sensitivity for moderate-to-severe sleep apnea; specificity for normal-to-mild sleep apnea; proportion of paired (Breathing Disturbance, reference AHI) within pre-specified performance zone

standards: FDA's 2023 Guidance, "Content of Premarket Submissions for Device Software Functions, FDA's 2023 Guidance, "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions, 2016 FDA Guidance "Applying Human Factors and Usability Engineering to Medical Devices"

Reported performance (2 observations)

sensitivity66.3CI [62.2%, 70.3%]
source quote (p.12)
“The sensitivity of notifications for subjects with moderate-to-severe sleep apnea (AHI 15) was 66.3%; 95% CI [62.2%, 70.3%].”
specificity98.5CI [98.0%, 99.0%]
source quote (p.12)
“The specificity of the notifications for those with normal-to-mild sleep apnea (AHI < 15) was 98.5%; 95% CI [98.0%, 99.0%].”

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
1
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 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/K240929