Sensitivity: 66.3% (95% CI [62.2%, 70.3%]); Specificity: 98.5% (95% CI [98.0%, 99.0%])
Training, Validation, Test, and Sequestration sets: >11,000 nights of concurrent reference and watch sensor data.
—
Prospective clinical study: 1,499 subjects using Nox T3s HSAT as reference.
—
Breathing disturbance estimation
Deep learning algorithm
—
91.4% of paired measurements within the pre-specified performance zone.
Training, Validation, Test, and Sequestration sets: >11,000 nights of concurrent reference and watch sensor data.
—
Prospective clinical study: 1,499 subjects using Nox T3s HSAT as reference.
—
Indications for Use
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. This feature is intended for over-the-counter (OTC) use by adults age 18 and over who have not previously received a sleep apnea diagnosis and is not intended to diagnose, treat, or aid in the management of sleep apnea. The absence of a notification is not intended to indicate the absence of sleep apnea.
Device Story
Software-only mobile medical application; runs on Apple Watch and iOS devices. Inputs: accelerometer sensor data collected during sleep. Processing: deep learning algorithm analyzes data in 30-day windows to identify breathing disturbance patterns. Output: user notification if patterns suggest moderate-to-severe sleep apnea; visualizations of breathing disturbance data. Used in home environment by patients; opportunistic background processing requires no active user initiation. Healthcare providers use output to guide clinical evaluation; device does not provide standalone diagnosis. Benefits: early identification of potential sleep apnea risk in previously undiagnosed individuals.
Clinical Evidence
Prospective study (N=1,499) compared SANF against Nox T3s HSAT reference. Sensitivity for moderate-to-severe sleep apnea (AHI ≥ 15) was 66.3% (95% CI: 62.2%, 70.3%); specificity for normal-to-mild (AHI < 15) was 98.5% (95% CI: 98.0%, 99.0%). 91.4% of paired breathing disturbance estimates fell within pre-specified performance zones.
Technological Characteristics
Software-only mobile medical application. Sensing principle: accelerometer-based breathing disturbance detection. Connectivity: iOS/Apple Watch ecosystem. Algorithm: deep learning. No hardware components; operates on general-purpose computing platforms. Cybersecurity: conforms to Section 524B of FD&C Act and FDA 2023 guidance.
Indications for Use
Indicated for adults age 18+ without prior sleep apnea diagnosis to identify patterns of breathing disturbances suggestive of moderate-to-severe sleep apnea via OTC notification. Not for diagnosis, treatment, or management of sleep apnea. Absence of notification does not indicate absence of sleep apnea.
Regulatory Classification
Identification
An over-the-counter (OTC) software-only, mobile medical application operating on a compatible Samsung Galaxy Watch and Phone. It is intended to detect signs of moderate to severe obstructive sleep apnea in the form of significant breathing disruptions in adult users 22 years and older, over a two-night monitoring period. It uses software algorithms to analyze input sensor signals (PPG and actigraphy) to provide a risk assessment for sleep apnea. It is not intended to provide a standalone diagnosis, replace traditional methods of diagnosis (e.g., polysomnography), assist clinicians in diagnosing sleep disorders, or be used as an apnea monitor.
Special Controls
In combination with the general controls of the FD&C Act, the over-the-counter device to assess risk of sleep apnea is subject to the following special controls:
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Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food & Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
September 13, 2024
Apple Inc. Lynda Ikejimba Principal Regulatory Affairs Associate One Apple Park Way Cupertino, California 95014
Re: K240929
Trade/Device Name: Sleep Apnea Notification Feature (SANF) Regulation Number: 21 CFR 868.2378 Regulation Name: Over-the-counter device to assess risk of sleep apnea Regulatory Class: Class II Product Code: QZW Dated: April 4, 2024 Received: April 4, 2024
Dear Lynda Ikejimba:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
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
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CONTROL PLAN" version 1.0. Under section 515C(b)(1) of the Act, a new premarket notification is not required for a change to a device cleared under section 510(k) of the Act, if such change is consistent with an established PCCP granted pursuant to section 515C(b)(2) of the Act. Under 21 CFR 807.81(a)(3), a new premarket notification is required if there is a major change or modification in the intended use of a device, or if there is a change or modification in a device that could significantly affect the safety or effectiveness of the device, e.g., a significant change or modification in design, material, chemical composition, energy source, or manufacturing process. Accordingly, if deviations from the established PCCP result in a major change or modification in the intended use of the device, or result in a change or modification in the device that could significantly affect the safety or effectiveness of the a new premarket notification would be required consistent with section 515C(b)(1) of the Act and 21 CFR 807.81(a)(3). Failure to submit such a premarket submission would constitute adulteration and misbranding under sections 501(f)(1)(B) and 502(o) of the Act, respectively.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review. the OS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rue"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
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Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
# Rachana Visaria -S
Rachana Visaria, Ph.D. Assistant Director DHT1C: Division of Anesthesia. Respiratory, and Sleep Devices OHT1: Office of Ophthalmic, Anesthesia, Respiratory, ENT, and Dental Devices Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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### Indications for Use
Submission Number (if known)
K240929 Device Name
Sleep Apnea Notification Feature (SANF)
Indications for Use (Describe)
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. This feature is intended for over-the-counter (OTC) use by adults age 18 and over who have not previously received a sleep apnea diagnosis and is not intended to diagnose, treat, or aid in the management of sleep apnea. The absence of a notification is not intended to indicate the absence of sleep apnea.
Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)
X Over-The-Counter Use (21 CFR 801 Subpart C)
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# 510(k) Summary
This summary of 5 l 0(k) safety and effectiveness information is submitted in accordance with the requirements of 21 CFR §807.92:
### 1. Submitter
| Applicant | Apple Inc.<br>One Apple Park Way<br>Cupertino, CA 95014 |
|-----------------------------|----------------------------------------------------------------------------------------------------|
| Submission<br>Correspondent | Lynda Ikejimba, PhD<br>Regulatory Affairs<br>Phone: (669) 227-8858<br>Email: lc_ikejimba@apple.com |
| Secondary<br>Correspondent | Kevin Go<br>Regulatory Affairs<br>Phone: (669) 225-1032<br>Email: kevin_f_go@apple.com |
| Date Prepared | Sept 13, 2024 |
# 2. Device Names and Classifications
### Subject Device:
| Name of Device | Sleep Apnea Notification Feature (SANF) |
|---------------------|---------------------------------------------------------------------------|
| Classification Name | Over-the-counter device to assess risk of sleep apnea, 21 CFR<br>868.2378 |
| Regulatory Class | Class II |
| Product Code | QZW |
| 510(k) Review Panel | Anesthesiology |
# 3. Predicate Device
| Predicate | Value |
|--------------|------------------------------|
| Manufacturer | Samsung Electronics Co., Ltd |
| Trade Name | Sleep Apnea Feature |
| 510(k) | DEN230041 |
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### 4. Device Description
The Sleep Apnea Notification Feature (SANF) is an over-the-counter mobile medical application (MMA) intended to identify patterns of breathing disturbances suggestive of moderate-to-severe sleep apnea and provide a notification to the user. SANF is intended to run on compatible iOS (e.g. iPhone, iPad) and Apple Watch platforms. Users set up SANF and view their health data on the iOS platform. Prior to use, users must undergo educational onboarding. SANF uses accelerometer sensor data collected by the Apple Watch to calculate breathing disturbance values while a user is asleep. Breathing disturbances describe transient changes in breathing patterns, such as temporary breathing interruptions.
Breathing disturbance data is analyzed in discrete, consecutive 30-day evaluation windows, If patterns consistent with moderate-to-severe sleep apnea are identified within the 30-day evaluation window, the user is notified. SANF provides visualizations depicting the user's breathing disturbance data over various time scales. SANF is not intended to provide instantaneous measurements. Instead, once activated, SANF runs opportunistically in the background receiving signals from Apple Watch sensors for processing.
### 5. Indications for Use
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. This feature is intended for over-the-counter (OTC) use by adults age 18 and over who have not previously received a sleep apnea diagnosis and is not intended to diagnose, treat, or aid in the management of sleep apnea. The absence of a notification is not intended to indicate the absence of sleep apnea.
### 6. Comparison with the Predicate Device
SANF and the predicate device (DEN230041) have the same intended use, technological characteristics, and principles of operation, and the difference in indications does not represent a new intended use. Both the subject and predicate devices are software-only mobile medical applications intended to detect signs of moderate-to-severe sleep apnea for individuals who have not been previously diagnosed with sleep apnea and are not intended to provide a standalone diagnosis.
The subject device contains some differences in technological characteristics:
- The subject device is compatible with Apple products (i.e., iOS device, Apple Watch), while ● the predicate is compatible with Samsung products (i.e., Galaxy Watch and Phone).
- . The subject device utilizes passive, opportunistic detection to monitor the user over a 30day period, and only alerts the user if it detects signs of sleep apnea. The predicate device provides an on-demand two-day assessment, and returns either a positive or negative finding to the user.
- . The subject device utilizes accelerometer sensor data while the predicate device utilizes blood oxygen sensor data.
The differences in technological characteristics described above do not raise new questions of safety or effectiveness. The differences can properly be evaluated through the special controls
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established in 21 CFR 868.2378. The subject device has been appropriately verified and validated through non-clinical and clinical testing to ensure that the device is substantially equivalent to the predicate. A complete comparison of the subject and predicate device can be found in Table 1 below.
| Item | Subject Device<br>Sleep Apnea Notification Feature | Predicate Device<br>(DEN230041) |
|--------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Device Name | Sleep Apnea Notification Feature<br>(SANF) | Sleep Apnea Feature |
| Manufacturer | Apple Inc. | Samsung Electronics Co., Ltd |
| Regulation Number | 21 CFR 868.2378 | 21 CFR 868.2378 |
| Product Code | QZW | QZW |
| Regulation Name | Over-the-counter device to assess risk of sleep apnea | Over-the-counter device to assess risk of sleep apnea. |
| Device<br>Classification | Class II | Class II |
| OTC/Prescription | OTC | OTC |
| Intended Use | An over-the-counter device to assess<br>risk of sleep apnea intended to provide<br>a notification of the risk of sleep apnea<br>in users who have not been previously<br>diagnosed with sleep apnea. This<br>device uses software algorithms to<br>analyze input sensor signals and<br>provide a risk assessment for sleep<br>apnea. It is not intended to provide a<br>standalone diagnosis, replace traditional<br>methods of diagnosis (e.g.,<br>polysomnography), assist clinicians in<br>diagnosing sleep disorders, or be used<br>as an apnea monitor. | An over-the-counter device to assess<br>risk of sleep apnea is intended to<br>provide a notification of the risk of sleep<br>apnea in users who have not been<br>previously diagnosed with sleep apnea.<br>This device uses software algorithms to<br>analyze input sensor signals and<br>provide a risk assessment for sleep<br>apnea. It is not intended to provide a<br>standalone diagnosis, replace traditional<br>methods of diagnosis (e.g.,<br>polysomnography), assist clinicians in<br>diagnosing sleep disorders, or be used<br>as an apnea monitor. |
| Table 1 : SANF Comparison with the Predicate | | |
|----------------------------------------------|--|--|
| | | |
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| Item | Subject Device<br>Sleep Apnea Notification Feature | Predicate Device<br>(DEN230041) |
|---------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Indications for Use | The Sleep Apnea Notification Feature<br>(SANF) is a software-only mobile<br>medical application that analyzes Apple<br>Watch sensor data to identify patterns<br>of breathing disturbances suggestive of<br>moderate-to-severe sleep apnea and<br>provides a notification to the user. This<br>feature is intended for over-the-counter<br>(OTC) use by adults age 18 and over<br>who have not previously received a<br>sleep apnea diagnosis and is not<br>intended to diagnose, treat, or aid in the<br>management of sleep apnea. The<br>absence of a notification is not intended<br>to indicate the absence of sleep apnea. | The Sleep Apnea Feature is an over-<br>the-counter (OTC) software-only,<br>mobile medical application operating on<br>a compatible Samsung Galaxy Watch<br>and Phone. This feature is intended to<br>detect signs of moderate to severe<br>obstructive sleep apnea in the form of<br>significant breathing disruptions in adult<br>users 22 years and older, over a two-<br>night monitoring period. It is intended<br>for on demand use. This feature is not<br>intended for users who have previously<br>been diagnosed with sleep apnea.<br>Users should not use this feature to<br>replace traditional methods of diagnosis<br>and treatment by a qualified clinician.<br>The data provided by this device is also<br>not intended to assist clinicians in<br>diagnosing sleep disorders. |
| Principle of<br>Operation | SANF uses software algorithms to<br>analyze input accelerometer sensor<br>signals and provide a risk assessment<br>for sleep apnea. | The Sleep Apnea Feature uses software<br>algorithms to analyze input blood<br>oxygen sensor signals and provide a<br>risk assessment for sleep apnea. |
| Overall Device<br>Design | A software-only device, and uses<br>software algorithms to analyze input<br>sensor signals from a general purpose<br>computing platform and provide a risk<br>assessment for sleep apnea.<br><br>Assessments are based on sensor data<br>collected over 30-day periods. The<br>device is intended to provide<br>opportunistic detection of sleep apnea,<br>such that after initial enrollment no user<br>interaction is required for the device to<br>perform as intended. | A software-only device, and uses<br>software algorithms to analyze input<br>sensor signals from a general purpose<br>computing platform and provide a risk<br>assessment for sleep apnea.<br><br>Assessments are based on sensor data<br>collected over a 2-day period. The<br>device is intended to provide on<br>demand assessments to detect signs of<br>sleep apnea, such that a user must<br>actively choose to initiate a monitoring<br>period. |
| Use Environment | Over-the-counter | Over-the-counter |
| Device Components | Software-only | Software-only |
| Device Input | Accelerometer data | Blood oxygen level (SpO2) data |
| Item | Subject Device<br>Sleep Apnea Notification Feature | Predicate Device<br>(DEN230041) |
| Clinical<br>Performance | The performance was optimized for<br>high specificity given SANF is designed<br>as an opportunistic detection feature<br>(i.e., passive, recurring).<br>Sensitivity: 66.3%<br>95% CI [62.2%, 70.3%]<br>Specificity: 98.5%<br>95% CI [98.0%, 99.0%] | Sensitivity: 82.7%<br>95% CI [76.7%, 87.6%]<br>Specificity: 87.7%<br>95% CI [83.1%, 91.4%] |
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# 7. Summary of Non-Clinical Testing
### Algorithm Development
SANF includes a deep learning algorithm to identify breathing disturbances using accelerometer sensor data from Apple Watch. The model was trained on Apple Watch accelerometer signals collected during sleep sessions with concurrent in-lab polysomnography (PSG) and Home Sleep Apnea Test (HSAT) reference recordings. The algorithm development dataset included over II,000 nights of concurrent reference and watch sensor data. The distribution of sleep apnea classifications in this dataset was broad and spanned all four clinically defined categories of sleep apnea: normal (AHI 0 to <5), mild (AHI 5 to <15), moderate (AHI 15 to <30), and severe (AHI >30). For the purposes of algorithm development, data from the studies was pooled and split into four sets: Training, Validation, Test, and Sequestration. The model was trained on the Training set, with the Validation set used for early stopping and threshold selection. The model was then evaluated on the Test set at regular intervals during model development was complete and the model was locked, it was evaluated on the Sequestration set as a last test to ensure it had not been over-fit to the training data. This process ensured no subject overlap and matching distributions of sex, age, BMI, and disease severity. The development data included a diverse group of subjects with respect to demographic factors (e.g., age, AHI, race, ethnicity, and BMI) representative of the intended use population.
### Non-clinical Testing Summary
Apple conducted the necessary non-clinical testing on SANF with passing results supporting a determination of substantial equivalence. Non-clinical testing conducted included the following:
### Software Verification and Validation
Software verification and validation was conducted in accordance with Apple's robust Quality Management System and documented to address the recommendations in FDA's 2023 Guidance, "Content of Premarket Submissions for Device Software Functions." SANF was determined to require a Basic Documentation Level. Apple's good software engineering practices, as demonstrated through the submission's documentation, supports a conclusion that SANF was appropriately designed, verified, and validated.
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### Cybersecurity
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.
### Human Factors Validation
The Sleep Apnea Notification Feature was found to be safe and effective as compared to the predicate for the intended users, uses, and use environments. This conclusion is supported by iterative human factors analyses and evaluations on the device, resulting design modifications and the analysis of the summative validation testing results as recommended by 2016 FDA Guidance , "Applying Human Factors and Usability Engineering to Medical Devices".
### General Purpose Computing Platform Assessment
SANF is a software-only device available on compatible general purpose computing platforms (e.g. Apple Watch); therefore, medical device hardware testing is not applicable. However, as a multiple function device product, the impact of the general purpose computing platform on SANF was assessed per FDA's 2020 Guidance, "Multiple Function Device Products: Policy and Considerations" and determined to be acceptable. This is consistent with the impact assessment of other Apple medical device features made available on Apple Watch, such as the Irregular Rhythm Notification Feature (K231173) and the Atrial Fibrillation History Feature (K213971).
### 8. Summary of Clinical Testing
The performance of the Sleep Apnea Notification Feature was validated in a prospective, nonsignificant risk study enrolling 1,499 subjects from several sites across the United States. The purpose of the study was to evaluate the performance of SANF using the Nox T3s home sleep apnea testing (HSAT) device (K192469) as a reference device. The study enrolled subjects across the spectrum of sleep apnea severity classifications, with a broad distribution across each of the following AHI categories using the "4%" hypopnea scoring rule: 559 normal subjects (AHI < 5), 362 mild subjects (5 ≤ AHI ≤ 15), 216 moderate subjects (15 ≤ AHI < 30), and 201 severe subjects (AHI ≥ 30), plus 161 subjects with missing HSAT reference. Subjects were also enrolled based across a broad range of demographic factors, including enrollment targets for age, sex, BMI, skin tone, race, and ethnicity subgroups to ensure the study population was representative of the intended user population. Study demographic characteristics are summarized in Table 2 below.
| N = 1,499 | |
|-------------------|-------------|
| Age Group (years) | |
| 18-49 | 855 (57.0%) |
| 50-64 | 491 (32.8%) |
| ≥65 | 153 (10.2%) |
| Sex | |
### Table 2: SANF Clinical Study Subject Demographics
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| Female | 847 (56.5%) |
|------------------------|---------------|
| Male | 652 (43.5%) |
| Ethnicity | |
| Hispanic or Latino | 181 (12.1%) |
| Non-Hispanic or Latino | 1,318 (87.9%) |
{11}------------------------------------------------
| Race | |
|----------------------------------------------|---------------|
| American Indian or Alaska<br>Native | 25 (1.7%) |
| Asian | 103 (6.9%) |
| Black or African American | 347 (23.1%) |
| Native Hawaiian or Other<br>Pacific Islander | 3 (0.2%) |
| White | 1,021 (68.1%) |
Of the 1,499 enrolled subjects, 1,278 contributed to the notification performance analysis and 1,305 contributed to the breathing disturbance performance analysis. Those not included in the performance had insufficient Apple Watch data and/or reference data.
The sensitivity of notifications for subjects with moderate-to-severe sleep apnea (AHI ≥ 15) was 66.3%; 95% Cl [62.2%, 70.3%]. The specificity of the notifications for those with normal-to-mild sleep apnea (AHI
< 15) was 98.5%; 95% Cl [98.0%, 99.0%]. SANF did not falsely notify any subjects with normal AHI (AHI < 5). The performance was similar for identified sub-groups.
To assess performance of Breathing Disturbance estimates, Apple evaluated the proportion of paired (Breathing Disturbance, reference AHI). Of the total 1,305 subjects who had at least one paired measurement, 1,193 (91.4%) were within the pre-specified performance zone.
These results demonstrate that the Sleep Apnea Notification is effective in generating accurate notifications for moderate-to-severe sleep apnea and Breathing Disturbance values.
# 9. Predetermined Change Control Plan
The SANF contains a Predetermined Change Control Plan (PCCP), which complies with Section 3308 of the Food and Drug Omnibus Reform Act (FDORA) of 2022, enacted on December 29, 2022. The PCCP does not include provisions for implementation of adaptive algorithms that will continuously learn in the field. All algorithm modifications will be trained, and locked prior to release of the software to the field. A procedure has also been established for updating the Instructions for Use in order to inform users about algorithm changes implemented under this FDAauthorized PCCP, including a summary of the changes, a characterization of algorithm performance, and the availability and compatibility of the feature. Apple will publish updated Instructions for Use on its website and make them accessible within the Health App.
The PCCP specifies possible modifications to the device software as well as verification and validation activities in place to implement the changes in a controlled manner such that the modified device remains as safe and effective as the predicate device. The PCCP includes a specific list of potential software modifications defining the region of potential changes that can be made to the algorithms in the device. Details of the potential changes are summarized in Table
{12}------------------------------------------------
3 below. The modification protocol incorporates impact assessment considerations and specifies requirements for data management, including data sources, collection, storage, and sequestration, as well as documentation and data re-use practices. Specific test methods are specified in the PCCP to establish substantial equivalence the Sleep Apnea Notification Feature and include sample size determination, analysis methods, and acceptance criteria. To help ensure validation test datasets are representative of the intended use population, each will meet minimum demographic requirements for age, sex, race, BMI and ethnicity.
| | Detailed List of Changes | Requirements | Test Method |
|-------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Modifications to<br>Breathing<br>Disturbances (BD)<br>computation | • Adjust the operating point<br>• Re-train algorithm with<br>additional datasets while<br>maintaining the same algorithm<br>architecture and number of<br>parameters.<br>• Revise signal input module<br>• Add additional classifier outputs<br>• Modifications to signal quality<br>and post-processing modules | • No change to input type<br>• No change to output type<br>• No concurrent change to<br>other modules<br>• No change to intended use<br>of device<br>• Can be fully verified and/or<br>validated by requirements of<br>the modification protocol | Verification of BD<br>accuracy and<br>substantial<br>equivalence in<br>notification-level<br>sensitivity and<br>specificity when<br>compared to the<br>performance of SANF<br>1.0 |
| Modifications to sleep<br>apnea estimation | • Modify the number of nightly BD<br>readings required to surface a<br>notification<br>• Reduce the interval of the<br>notification window<br>• Modify logic for surfacing a<br>notification based on BDs | | Substantial<br>equivalence in<br>sensitivity and<br>specificity when<br>compared to the<br>performance of SANF<br>1.0 |
Table 3: Proposed modifications to the SANF under the PCCP
# 10. Conclusion
The Sleep Apnea Notification Feature is substantially equivalent to the predicate device as they are identical with respect to intended use and there are no differences in technological or performance characteristics that raise different questions of safety and effectiveness.
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Learn the FDA Browser
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.