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The IRNF is a software-only mobile medical application that is intended to be used with the Apple Watch. The feature analyzes pulse rate data to identify episodes of irregular heart rhythms suggestive of atrial fibrillation (AFib) and provides a notification to the user. The feature is intended for over-the-counter (OTC) use. It is not intended to provide a notification on every episode of irregular rhythm suggestive of AFib and the absence of a notification is not intended to indicate no disease process is present; rather the feature is intended to opportunistically surface a notification of possible AFib when sufficient data are available for analysis. These data are only captured when the user is still. Along with the user's risk factors the feature can be used to supplement the decision for AFib screening. The feature is not intended to replace traditional methods of diagnosis or treatment.

The feature has not been tested for and is not intended for use in people under 22 years of age. It is also not intended for use in individuals previously diagnosed with AFib.

IRNF 2.0 is comprised of a pair of mobile medical apps - One on Apple Watch and the other on the iPhone.

IRNE 2.0 is intended to analyze pulse rate data collected by the Apple Watch PPG sensor on Apple Watch Series 3-8, Series SE, and Apple Watch Ultra to identify episodes of irregular heart rhythms consistent with AFib and provides a notification to the user. It is a background screening tool and there is no way for a user to initiate analysis of pulse rate data. IRNF 2.0 iPhone App is part of the Health App, which allows users to store, manage, and share health and fitness data, and comes pre-installed on every iPhone.

IRNF 2.0 Watch App refers to the tachogram classification algorithm, confirmation cycle algorithm, and the AF notification generation. If an irreqular heart rhythm consistent with AFib is identified, IRNF 2.0 Watch App will transfer the AFib notification to IRNF 2.0 iPhone App through HealthKit sync. In addition to indicating the finding of signs of AFib, the notification will encourage the user to seek medical care.

IRNF 2.0 iPhone App contains the on-boarding and educational materials that a user must review prior to enabling AFib notifications. IRNF 2.0 iPhone App is designed to work in combination with IRNF 2.0 Watch App and will display a history of all prior AFib notifications. The user is also able to view a list of times when each of the irregular tachograms contributing to the notification was generated.

The provided text describes the Irregular Rhythm Notification Feature (IRNF) 2.0. However, the document provided is a 510(k) summary and clearance letter for a Predetermined Change Control Plan (PCCP) for IRNF 2.0, rather than a standalone study proving the device meets acceptance criteria for initial clearance.

The document indicates that the subject device (IRNF 2.0) is identical to its predicate device (also IRNF 2.0, K212516), with the only difference being the implementation of a PCCP. This PCCP outlines anticipated modifications to the software and the methods for implementing those changes. Therefore, the acceptance criteria and study data for the initial clearance of IRNF 2.0 (K212516) would be the most relevant information, which is not entirely detailed in this document.

However, the PCCP does specify test methods and acceptance criteria that will be used to demonstrate substantial equivalence for future modifications made under the plan. I will extract information primarily related to these future modification criteria and the study that would be performed to meet them.

Here's a breakdown based on the provided text, focusing on the PCCP and what it implies for future studies:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria described here are for future modifications to the algorithm under the PCCP, showing substantial equivalence to the performance of the existing IRNF 2.0. The document does not provide the absolute performance of IRNF 2.0 itself in this section, but rather the performance target for modified algorithms relative to IRNF 2.0.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set: The document states that for future modifications under the PCCP, "each will meet minimum demographic requirements for age, sex, race, and skin tone derived from the demographics of the United States." It does not specify an exact numerical sample size for the test set.
• Data Provenance: The document implies that validation test datasets will be "representative of the intended use population" and mentions "demographics of the United States." This suggests the data will primarily be from the United States. It does not explicitly state whether the data will be retrospective or prospective for these future validation activities.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

The document does not specify the number of experts or their qualifications for establishing ground truth, either for the initial clearance of IRNF 2.0 or for the future modifications under the PCCP.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method (e.g., 2+1, 3+1, none) for the test set, either for the initial clearance of IRNF 2.0 or for the future modifications under the PCCP.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

The document does not mention a Multi-Reader Multi-Case (MRMC) comparative effectiveness study. The IRNF is described as a "software-only mobile medical application" providing notifications to the user, not a tool for human readers to interpret.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Yes, the document implies that standalone performance studies were done (or will be done for future modifications). The device is described as "software-only" and "analyzes pulse rate data... and provides a notification to the user." The acceptance criteria for future modifications explicitly refer to the algorithm's sensitivity, specificity, and positive predictive value, which are metrics of standalone algorithm performance.

7. The Type of Ground Truth Used

The document does not explicitly state the type of ground truth used (e.g., expert consensus, pathology, outcomes data). In the context of "irregular heart rhythms suggestive of atrial fibrillation (AFib)," the ground truth would typically be established by a gold standard method such as a 12-lead ECG interpreted by a cardiologist, or a continuous ECG monitor.

8. The Sample Size for the Training Set

The document states that for future modifications to the tachogram classification algorithm, the plan is to "retrain algorithm with additional datasets." It does not specify the sample size for the training set, either for the original IRNF 2.0 or for the "additional datasets" mentioned for future retraining.

9. How the Ground Truth for the Training Set Was Established

The document does not specify how the ground truth for the training set was established, either for the original IRNF 2.0 or for future retraining datasets.

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The Atrial Fibrillation (AFib) History Feature is an over-the-counter ("OTC") software-only mobile medical application intended for users 22 years of age and over who have a diagnosis of atrial fibrillation (AFib). The feature opportunistically analyzes pulse rate data to identify episodes of irregular heart rhythms suggestive of AFib and provides the user with a retrospective estimate of AFib burden (a measure of the spent in AFib during past Apple Watch wear).

The feature also tracks and trends estimated AFib burden over time, and includes lifestyle data visualizations to enable users to understand the impact of certain aspects of their AFib. It is not intended to provide individual irregular rhythm notifications or to replace traditional methods of diagnosis, treatment, or MFib.

The feature is intended for use with the Apple Watch and the Health app on iPhone.

The Atrial Fibrillation History Feature (AFib History Feature) is comprised of a pair of mobile medical apps - one on Apple Watch and the other on the iPhone.

The AFib History Feature is intended to analyze pulse rate data collected by the Apple Watch PPG sensor on Apple Watch Series 4. Series 5. and SE to identify episodes of irregular heart rhythms consistent with AFib and provides the user with a retrospective estimate of AFib burden (a measure of the amount of time spent in AFib during past Apple Watch wear).

The AFib History Feature uses PPG pulse rhythm data from compatible Apple Watches. Apple Watch uses green LED lights paired with light-sensitive photodiodes to detect relative changes in the amount of blood flowing through a user's wrist at any given moment. When the heart beats it sends a pressure wave down the vasculature, causing a momentary increase in blood volume when it passes by the sensor. By monitoring these changes in blood flow, the sensor detects individual pulses when they reach the peripherv and thereby measure beat-to-beat intervals.

The AFib History Feature iPhone App is part of the Health App. which allows users to store, manage, and share health and fitness data, and comes pre-installed on every iPhone.

The AFib History Feature provides users visualizations of AFib burden estimate data alongside clinically relevant lifestyle data and presents estimates of AFib burden in three different ways. These visualizations empower users to observe and understand the impact of lifestyle on their AFib burden, and to better understand their condition generally.

• · Weekly Estimate an estimate of the amount of time a user was in Atrial Fibrillation over the past calendar week during watch wear, presented to the user as a percentage.
• Day of Week Estimate an estimate of the amount of time a user was in Atrial Fibrillation on each day of the week over the previous 42 days during watch wear, presented to the user as a percentage. That is, all Mondays over the past 42 days, all Tuesdays over the past 42 days.
• Time of Day Estimate an estimate of the amount of time a user was in Atrial Fibrillation on 4-hour segments of the day over the previous 42 days during watch wear, presented to the user as a percentage. That is, all 12 am - 4 am segments over the past 42 days, all 4 am - 8 am segments over the past 42 days.

The AFib History Feature is intended to serve as an extension of the predicate Irregular Rhythm Notification feature, but has been optimized for users with a diagnosis of Afib.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA document.

The document describes the Atrial Fibrillation History Feature, a software-only mobile medical application intended for users 22 years and older with a diagnosed Atrial Fibrillation (AFib). The feature analyzes pulse rate data from Apple Watch to provide a retrospective estimate of AFib burden.

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly list "acceptance criteria" in a definitive table format with pass/fail thresholds. Instead, it presents performance metrics from development and clinical studies for the classification algorithm and for the AFib burden estimation.

Implicit Acceptance Criteria (Derived from Performance Data and Context):

• Rhythm Classification Algorithm Performance: High sensitivity and specificity in differentiating AFib from non-AFib rhythms.
• AFib Burden Estimation Accuracy: Close agreement between the device's weekly AFib burden estimate and the reference method.

Reported Device Performance:

2. Sample Size for the Test Set and Data Provenance

The document refers to two main test sets:

• Development Studies Test Set: Data used for evaluating the model during development and as a "last test" on a "Sequestration set" (which functions as a final test set after model locking).
  • Sample Size: Part of "over 2500 subjects" and "over 3 million pulse rate recordings". The exact number of subjects or recordings specifically in the test/sequestration sets for the classification algorithm performance is not individually quantified beyond being a split of the total development data.
  • Data Provenance: Not explicitly stated (e.g., country of origin, specific institutions). It mentions "demographically diverse populations" in recruitment. The studies are described as "development studies," implying they were specifically conducted for the purpose of algorithm development. The data appears to be prospective given it was collected from recruited subjects.
• Clinical Validation Study Test Set:
  • Sample Size: 413 enrolled subjects for the study. 280 subjects contributed data to the primary endpoint analysis for AFib burden estimation.
  • Data Provenance: Not explicitly stated (e.g., country/region). It describes "enrolled subjects wore an Apple Watch and a reference electrocardiogram (ECG) patch concurrently for up to 13 days," indicating a prospective clinical study specifically for this validation.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document refers to "reference electrocardiogram (ECG) patch concurrently" and "reference weekly burden" for establishing ground truth, both in the development studies and the clinical validation study.

• Number of Experts: Not explicitly stated.
• Qualifications of Experts: Not explicitly stated. However, the use of "reference electrocardiogram (ECG)" strongly implies that medical professionals (e.g., cardiologists, electrophysiologists) would be involved in reading and interpreting these ECGs to establish the ground truth for AFib presence and burden.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated. The document refers to "reference electrocardiogram (ECG)" as the ground truth. It's common practice for ECG interpretations, especially in clinical studies, to involve review by multiple experts or an adjudication committee, but the specific method (e.g., 2+1, 3+1) is not detailed.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was an MRMC Comparative Effectiveness Study Done? No, an MRMC study comparing human readers with and without AI assistance was not described. The study focuses on the standalone performance of the device's algorithm against a reference standard (ECG).

• Effect Size of Human Reader Improvement: Not applicable, as no MRMC study was performed.

6. Standalone Performance (Algorithm Only)

• Was a Standalone Study Done? Yes. The performance metrics presented for both the rhythm classification algorithm and the AFib burden estimation in the development and clinical studies are indicative of the device's standalone (algorithm-only, without human-in-the-loop performance) performance. The device is a "software-only mobile medical application," and its output (AFib burden estimate) is directly compared to the reference standard.

7. Type of Ground Truth Used

• Type of Ground Truth: The primary ground truth for both the classification algorithm development/testing and the clinical validation of AFib burden estimation was established using reference electrocardiogram (ECG) data. This is considered a high-fidelity diagnostic standard for cardiac rhythm.

8. Sample Size for the Training Set

• Training Set Sample Size: The algorithm was "trained extensively using data collected in a number of development studies. In total, the studies included over 2500 subjects and collected over 3 million pulse rate recordings." The training set was a portion of this total dataset, with the data "split into four sets...: Training, Validation, Test, and Sequestration sets." The exact number of subjects or recordings specifically in the training set is not provided separately.

9. How the Ground Truth for the Training Set Was Established

• Ground Truth Establishment for Training Set: "The rhythm classification algorithm uses a convolutional neural network based architecture and was trained extensively using data collected in a number of development studies." Similar to the test sets, the ground truth for rhythms in the training data would also have been established using reference electrocardiogram (ECG) data. The document implies that these development studies also involved the collection of "pulse rate recordings on a variety of rhythms including: atrial fibrillation, normal sinus rhythm, sinus arrhythmia, and other ectopic beats (PVCs, PACs)," which would necessitate ECG interpretation to label these rhythms for training the algorithm.

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The Fitbit Irregular Rhythm Notifications is a software-only mobile medical application that is intended to be used with compatible consumer wrist-worn products to analyze pulse rate data to identify episodes of irregular heart rhythms suggestive of atrial fibrillation (AFib) and provide a notification to the user.

The Fitbit Irregular Rhythm Notifications is intended for over-the-counter (OTC) use. It is not intended to provide a notification on every episode of irregular rhythm suggestive of AFib and the absence of a notification is not intended to indicate no disease process is present; rather the Fitbit Irregular Rhythm Notifications is intended to opportunistically surface a notification of possible AFib when sufficient data are available for analysis.

These data are only captured when the user's still. Along with the user's risk factors, the Fitbit irreqular Rhythm Notifications can be used to supplement the decision for AFib screening. The Fitbit Irregular Rhythm Notfications is not intended to replace traditional methods of diagnosis or treatment.

The Fitbit Irregular Rhythm Notifications has not been tested for use in people under 22 years of age. It is also not intended for use in individuals previously diagnosed with AFib.

The Fitbit Irreqular Rhythm Notifications consists of an algorithm that classifies pulse rate data, and a mobile application run within the Fitbit app that serves as the user interface (UI) and device display.

The Fitbit Irregular Rhythm Notifications leverages pulse rate data collected from compatible commercially available, general purpose wrist-worn products (e.g., smartwatch or fithess tracker). Photoplethysmograph (PPG) sensors consist of light-emitting diodes (LED) and photodiodes that detect changes in blood flow of a user's vasculature at any given moment. When the heart beats, it sends a pressure wave through the vasculature causing a blood flow increase. By monitoring the fluctuations the consumer wrist-worn products can measure pulse rate data. When the user is still the sensor detects when individual pulses reach the periphery (i.e., wrist) and measures beat-to-beat intervals.

If the analyzed data are consistent with signs of atrial fibrillation, a notification indicating that a heart rhythm showing signs suggestive of AFib will be displayed to the user. The Fitbit Irregular Rhythm Notifications will only surface a notification of a heart rhythm showing signs of AFib once in a 24-hour period.

The Fitbit Irregular Rhythm Notifications mobile app functions within the Fitbit consumer application and is run on a compatible, user-provided general purpose mobile computing product (e.g., smartphone or tablet). The Fitbit Irregular Rhythm Notifications mobile app serves as the display/user interface for the Fitbit Irregular Rhythm Notifications.

Acceptance Criteria and Device Performance for Fitbit Irregular Rhythm Notifications

1. Table of Acceptance Criteria and Reported Device Performance

The provided document focuses on the substantial equivalence to a predicate device rather than explicitly stating acceptance criteria as a numerical target. However, it details the performance metrics demonstrated by the clinical study to support this equivalence. The key performance metric reported is the Positive Predictive Value (PPV) for AFib detection.

2. Sample Size and Data Provenance

• Test Set Sample Size: 225 subjects received a positive algorithm detection and wore an ECG patch for comparison.
• Data Provenance: The study recruited subjects from Fitbit's U.S. user population. The data is prospective, as users consented and were instructed to perform specific actions (schedule a telehealth visit, wear an ECG patch) following algorithm detection.

3. Number of Experts and Qualifications for Ground Truth

The document states that "Data gathered from the ECG patch was analyzed by medical professionals to determine whether signs of AFib were present." It does not specify the exact number of experts or their detailed qualifications (e.g., "radiologist with 10 years of experience").

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method like 2+1 or 3+1. It states that "data gathered from the ECG patch was analyzed by medical professionals to determine whether signs of AFib were present," implying a single assessment or a consensus process not detailed.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No Multi-Reader Multi-Case (MRMC) comparative effectiveness study was mentioned. The study focused on the standalone algorithm's performance against ECG ground truth, not on how human readers improve with or without AI assistance.

6. Standalone (Algorithm Only) Performance

Yes, a standalone performance study was conducted. The clinical study evaluated the Fitbit Irregular Rhythm Notifications algorithm's ability to identify irregular heart rhythms suggestive of AFib. The reported PPV of 98.2% is a measure of this standalone algorithmic performance.

7. Type of Ground Truth Used

The ground truth used for the test set was ECG patch data analyzed by medical professionals. This is a direct measure of cardiac electrical activity, considered a gold standard for AFib diagnosis.

8. Sample Size for the Training Set

The document does not specify the sample size for the training set. It mentions that the clinical study "recruited subjects from Fitbit's U.S. user population, inviting them to participate in a study. Upon consent, users had their PPG data analyzed for signs consistent with AFib by the algorithm." This describes part of the clinical validation, but not the training process or the data used for it.

9. How Ground Truth for the Training Set Was Established

The document does not provide details on how the ground truth for the training set was established. It only describes the ground truth establishment for the clinical validation test set (ECG patch data analyzed by medical professionals).

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The Irregular Rhythm Notification Feature is a software-only mobile medical application that is intended to be used with the Apple Watch. The feature analyzes pulse rate data to identify episodes of irregular heart rhythms suggestive of atrial fibrillation (AFib) and provides a notification to the user. The feature is intended for over-the-counter (OTC) use. It is not intended to provide a notification on every episode of irregular rhythm suggestive of AFib and the absence of a notification is not intended to indicate no disease process is present; rather is intended to opportunistically surface a notification of possible AFib when sufficient data are available for analysis. These data are only captured when the user is still. Along with the user's risk factors the feature can be used to supplement the decision for AFib screening. The feature is not intended to replace traditional methods of diagnosis or treatment.

The feature has not been tested for and is not in people under 22 years of age. It is also not intended for use in individuals previously diagnosed with AFib.

Irregular Rhythm Notification Feature 2.0 (IRNF 2.0) is comprised of a pair of mobile medical apps - One on Apple Watch and the other on the iPhone.

IRNF 2.0 is intended to analyze pulse rate data collected by the Apple Watch PPG sensor on Apple Watch Series 3. Series 4. Series 5. and SE to identify exisodes of irreqular heart rhythms consistent with AFib and provide a notification to the user. It is a background screening tool and there is no way for a user to initiate analysis of pulse rate data. IRNF 2.0 iPhone App is part of the Health App. which allows users to store, manage, and share health and fitness data, and comes pre-installed on every iPhone.

IRNF 2.0 Watch App refers to the rhythm classification algorithm, confirmation cvcle algorithm, and the AFib notification generation. If an irreqular heart rhythm consistent with Afib is identified and confirmed through the confirmation cycle, IRNF 2.0 Watch app will notify the user and transfer the AFib notification to the iPhone App through HealthKit sync. In addition to indicating the finding of signs of AFib, the notification will encourage the user to seek medical care.

IRNF 2.0 iPhone App contains the onboarding and educational materials that a user must review prior to use. IRNF 2.0 iPhone App is designed to work in combination with IRNF 2.0 Watch App and will display a history of all prior AFib notifications. The user is also able to view a list of times of the irreqular rhythms contributing to the notification.

The provided text describes the Irregular Rhythm Notification Feature (IRNF) 2.0 app, a software-only mobile medical application for detecting irregular heart rhythms suggestive of Atrial Fibrillation (AFib) using Apple Watch pulse rate data. Below is a detailed breakdown of the acceptance criteria and study proving the device meets them, based on the provided document:

Acceptance Criteria and Reported Device Performance

The document states that the clinical performance of the IRNF 2.0 app was assessed, with specific metrics reported:

Note: The document states "IRNF 2.0 person-level sensitivity (88.6%) and specificity (99.3%) were both demonstrated to be non-inferior to those of the predicate device." While a specific numerical acceptance criterion for sensitivity and specificity isn't explicitly listed, the demonstration of non-inferiority to the predicate device (which had a reported 78.9% sensitivity for concordant AFib and 98.2% for AFib and other clinically relevant arrhythmias) serves as the implicit acceptance benchmark.

Study Details

Here's the information about the study that proves the device meets the acceptance criteria:

2. Sample Size Used for the Test Set and Data Provenance:

• Test Set Sample Size: The clinical validation study (referred to as the "clinical study" under 5.7 Clinical Performance) involved 573 participants.
  • For the primary endpoint analysis, 432 participants contributed data to determine sensitivity, with 140 of these presenting with AFib.
  • 292 participants contributed data to the analysis of device specificity.
• Data Provenance: The document does not explicitly state the country of origin of the data. It is implied to be a prospective study, as it involved enrolled subjects wearing an Apple Watch and a reference ECG patch concurrently.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:

• The document does not specify the number or qualifications of experts used to establish the ground truth for the test set. It states that the reference was an "electrocardiogram (ECG) patch concurrently." This implies a medical standard for AFib diagnosis, but the human interpretation component by experts is not detailed.

4. Adjudication Method for the Test Set:

• The document does not describe a specific adjudication method (e.g., 2+1, 3+1) for the test set. The ground truth appears to be established directly from the reference ECG patch data.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

• No, an MRMC comparative effectiveness study involving human readers assisting with or without AI was not described for this device. The study primarily focuses on the standalone performance of the IRNF 2.0 app against a reference standard.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done:

• Yes, a standalone performance study was done. The reported sensitivity (88.6%) and specificity (99.3%) refer to the device's performance in identifying irregular heart rhythms suggestive of AFib based on pulse rate data, without human interpretation in the loop for the primary performance metrics. The feature provides a notification to the user, encouraging them to seek medical care, but its performance metrics are of the algorithm only.

7. The Type of Ground Truth Used:

• The ground truth for the clinical study was established using a reference electrocardiogram (ECG) patch, worn concurrently by participants. AFib was "identified on the reference ECG patch."

8. The Sample Size for the Training Set:

• The document states that the new rhythm classification algorithm "was trained extensively using data collected in a number of development studies." These studies "included over 2500 subjects." While the exact size of the training set itself is not broken out numerically, it's part of this larger "over 2500 subjects" pool. The data was split into Training, Validation, Testing, and Sequestration sets.

9. How the Ground Truth for the Training Set Was Established:

• The ground truth for the training set was established from "data collected in a number of development studies" that included "over 3 million pulse rate recordings on a variety of rhythms including: atrial fibrillation, normal sinus rhythm, sinus arrhythmia, and other ectopic beats (PVCs, PACs)."
• It's inferred that these "rhythms" were definitively classified to serve as ground truth for training the convolutional neural network. While the exact method of establishing ground truth for the training data (e.g., direct ECG correlation, expert annotation of ECGs) is not explicitly detailed, the mention of specific rhythm types suggests a medically validated classification.

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The Irregular Rhythm Notification Feature is a software-only mobile medical application that is intended to be used with the Apple Watch. The feature analyzes pulse rate data to identify episodes of irregular heart rhythms suggestive of atrial fibrillation (AFib) and provides a notification to the user. The feature is intended for over-the-counter (OTC) use. It is not intended to provide a notification on every episode of irregular rhythm suggestive of AFib and the absence of a notification is not intended to indicate no disease process is present; rather the feature is intended to opportunistically surface a notification of possible AFib when sufficient data are available for analysis. These data are only captured when the user is still. Along with the user's risk factors, the feature can be used to supplement the decision for AFib screening. The feature is not intended to replace traditional methods of diagnosis or treatment.

The feature has not been tested for and is not intended for use in people under 22 years of age. It is also not intended for use in individuals previously diagnosed with AFib.

The Irregular Rhythm Notification Feature comprises a pair of mobile medical apps, one on Apple Watch and the other on the iPhone. The Irregular Rhythm Notification Feature analyzes pulse rate data collected by the Apple Watch photoplethysmograph (PPG) sensor to identify episodes of irregular heart rhythms consistent with atrial fibrillation (referred to in this document as AF or AFib) and provides a notification to the user. It is a background screening tool and there is no way for a user to initiate analysis of pulse rate data. The Irregular Rhythm Notification Feature is part of the Health App, which allows users to store, manage, and share health and fitness data, and comes pre-installed on every iPhone. Users must opt-in and go through onboarding prior to use of the Irregular Rhythm Notification Feature.

The Irregular Rhythm Notification Feature is not intended to diagnose atrial fibrillation, and is not intended to be used to guide clinical treatment or care.

Here's a summary of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Device Performance for Irregular Rhythm Notification Feature

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly define a "table of acceptance criteria" with specific numerical targets in the same way an academic paper might. However, by synthesizing the "Clinical Study" section and the "Risks to Health" with their "Mitigation Measures," we can infer the key performance metrics and their achieved results. The pre-specified performance goal was explicitly stated for the primary endpoint.

2. Sample Size for the Test Set and Data Provenance

• Clinical Study Test Set:

  • Full Analysis Set (FAS): 269 subjects
  • Analyzable ECG monitor and tachogram data: 226 subjects (after exclusions)
  • Irregular tachograms recorded: 2634 (out of 10432 total tachograms during monitoring)
  • Subjects receiving at least one alert: 57 (25.2% of the 226 analyzable subjects)

• Data Provenance: The data was collected from a large, prospective, single-arm study conducted to investigate PPG data from Apple Watch for AF-related irregular heart rhythm identification. The sub-study enrolled participants who had already received one prior Irregular Rhythm notification. The country of origin is not explicitly stated but implies a broad user base given Apple's global reach, although the study itself was managed by Apple Inc. in Cupertino, CA.

• Bench Testing Test Set:

  • External Aggressor Condition Testing:
    • Riding in a car (vibration): 44 subjects, 1434 measurements
    • Targeted Hand + finger motions: 20 subjects, 246 measurements
    • Low perfusion: 102 subjects, 2461 measurements
    • Hand Tremors: 143 subjects, 936 measurements
  • Skin Tone Performance: 1124 subjects, 1.3 million measurements

• Human Factors and Usability Study Test Set: 37 participants (16 "Active Interest", 21 "Passive Interest")

3. Number of Experts and Qualifications for Ground Truth - Clinical Study

• Number of Experts: Unspecified exact number of independent cardiologists. It states "independent cardiologists" (plural).
• Qualifications: "Independent cardiologists" (no further details on experience or specialization, but the title implies appropriate medical expertise).

4. Adjudication Method for the Test Set - Clinical Study

The document states: "For each one-minute irregular rhythm episode (tachogram) identified by the software, the corresponding patch ECG recording was extracted and classified by independent cardiologists as either 'Sinus rhythm', 'AF', 'Unreadable', or 'Other Irregular Rhythm.'" This implies a single expert classification or a pre-defined process without explicitly stating a consensus method like 2+1 or 3+1.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No, an MRMC comparative effectiveness study was not done or reported comparing human readers with and without AI assistance. The clinical study focused on the standalone performance of the device's notification feature against an ambulatory ECG.

6. Standalone (Algorithm Only) Performance

• Yes, the primary clinical study was a standalone (algorithm only) performance evaluation. The device independently identified irregular rhythm episodes, which were then compared to the ground truth established by ambulatory ECG and cardiologist review. There was no human-in-the-loop component for the detection performance evaluation itself.

7. Type of Ground Truth Used

• Clinical Study:
  • For the primary and secondary endpoints related to AF detection, the ground truth was established by 7-day ambulatory patch ECG recordings, which were then "classified by independent cardiologists as either 'Sinus rhythm', 'AF', 'Unreadable', or 'Other Irregular Rhythm.'" This combines outcomes data (ECG) with expert consensus/interpretation.
• Bench Testing:
  • Functional performance: Compared to "commercial, FDA-cleared clinical ECG."
• Human Factors Study:
  • User comprehension: Observational data and subjective evaluations of user responses to questions.

8. Sample Size for the Training Set

The document does not explicitly state the sample size for the training set used to develop the Irregular Rhythm Notification Feature algorithm. It only describes the evaluation of the algorithm on the described test sets.

9. How the Ground Truth for the Training Set Was Established

The document does not explicitly describe how the ground truth for the training set was established. It focuses only on the validation study. It can be inferred that similar methods (ECG data interpreted by cardiologists) would likely have been used during development, but this is not detailed in the provided text.

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