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Loss of Pulse Detection is a software-only mobile medical application that is intended to be used with compatible consumer wrist-worn products to analyze pulse data to identify loss of pulse events and provide audio, visual, and haptic alerts to the user.

If the user remains unresponsive to these alerts, Loss of Pulse Detection will attempt to prompt a call to emergency services through the user's connected compatible hardware, such as a smartphone or smartwatch.

Loss of Pulse Detection is intended for over-the-counter (OTC) use. It is not intended to provide a notification on every loss of pulse event and the absence of an alert is not intended to indicate that no such event has occurred; rather the Loss of Pulse Detection is intended to opportunistically surface an alert of possible loss of pulsatility when sufficient data are available for analysis.

These data are only captured when the user is still. Loss of Pulse Detection is not intended to replace traditional methods of diagnosis, treatment, or monitoring.

Loss of Pulse Detection 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 a high risk for sudden cardiac death such as those with coronary artery disease, cardiomyopathy and/or unexplained syncope/fainting.

The Loss of Pulse detection SaMD is intended to be used for the detection of loss of pulse using photoplethysmography (PPG) and accelerometer sensors present in a wrist worn consumer wearable device. Upon detection of loss of pulse, when the smartwatch is worn, the feature will prompt haptic and audio alerts and notifications to the user and prompt the user's compatible hardware to call emergency services if the user is unresponsive to the notifications and alerts. This is an opt-in feature and will be off by default.

The Loss of Pulse detection SaMD comprises a software component that resides on the compatible consumer wearable device (from here on referenced as a "smartwatch") and a user facing mobile application that resides on general purpose compatible consumer mobile devices such as a smartphone (from here on referenced as "Smartphone"). The software component on the smartwatch analyzes pulse data collected by photoplethysmography (PPG) and accelerometry sensors from qualified smartwatch, using an algorithm employing digital signal processing (DSP) and features-based machine learning based on a convolutional neural network (CNN) to detect possible loss of pulse events.

The document provides the following information regarding the acceptance criteria and the study that proves the device meets the acceptance criteria for the Loss of Pulse Detection (LPD) software:

1. Table of Acceptance Criteria and Reported Device Performance

While explicit "acceptance criteria" are not presented in a formal table with specific thresholds, the document details the performance metrics achieved in the clinical validation study. The performance is primarily evaluated based on sensitivity and specificity.

2. Sample Sizes and Data Provenance

• Test Set (Clinical Validation Studies):

  • Primary Study: 135 participants for sensitivity evaluation (pulseless and pulsatile data).
  • Secondary Study: 21 participants for sensitivity evaluation (pulseless data).
  • Specificity Evaluation: 131 evaluable participants from the first study.
  • Data Provenance: The document states participants were from a "racially and ethnically diverse population, including adults of diverse sex and age." It does not specify the country of origin, nor explicitly state if it was retrospective or prospective, though clinical validation studies are generally prospective. The phrase "simulated loss of pulse events induced by an arterial occlusion model" suggests a controlled, prospective clinical study.

• Training and Validation Sets (Algorithm Development):

  • Training/Validation/Development Datasets: Over one hundred thousand hours of free-living data (from hundreds of participants who experienced no loss of pulse events) and data from 99 participants who experienced simulated loss of pulse events induced by an arterial occlusion model.
  • Data Provenance: The document states "The training, validation, and test splits used for development included data from diverse participants with varied age, sex, BMI, and skin tone." Again, country of origin is not specified. The data comprised both "free-living data" (suggesting retrospective real-world data) and "simulated loss of pulse events" (suggesting prospective controlled data).

3. Number of Experts and Qualifications for Ground Truth

The document does not explicitly state the number of experts used to establish ground truth or their specific qualifications (e.g., "radiologist with 10 years of experience"). For the clinical validation study where simulated loss of pulse was induced, it implies that the ground truth for "loss of pulse" was established directly through the experimental protocol (arterial occlusion model) rather than by expert review of physiological signals post-hoc. For "no loss of pulse" data, the ground truth is inherently the absence of such an event in free-living data of healthy individuals.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method (e.g., 2+1, 3+1) for the test set. Given that the ground truth for simulated loss of pulse was established by an induced event, and for pulsatile data by its natural occurrence, a separate adjudication method using human experts on the collected data is not described as part of the primary ground truth establishment.

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

An MRMC comparative effectiveness study was not explicitly mentioned or detailed. The study described focuses on the standalone performance of the algorithm and its ability to trigger an emergency call workflow, rather than comparing human reader performance with and without AI assistance. The "Real World Evidence Summary" indicates that human users interact with the device's alerts, but it does not quantify human improvement due to AI assistance in a comparative MRMC framework.

6. Standalone (Algorithm-Only) Performance

Yes, standalone performance was done. The reported sensitivity and specificity values (69.3% and 99.965%) represent the performance of the Loss of Pulse Detection algorithm. The clinical validation study was designed to evaluate the software's ability to detect loss of pulse events.

7. Type of Ground Truth Used

• For Loss of Pulse Events: Ground truth was established through simulated loss of pulse events induced by an arterial occlusion model. This is a physiological, objective method.
• For Absence of Loss of Pulse: Ground truth for pulsatile data was established by the absence of induced events in control participants and "free-living data across hundreds of participants who experienced no loss of pulse events." This relies on the natural state of healthy individuals.

8. Sample Size for the Training Set

The training set was part of a larger dataset used for algorithm development, which consisted of:

• "Over one hundred thousand hours of free-living data across hundreds of participants who experienced no loss of pulse events."
• Data from "99 participants who experienced simulated loss of pulse events induced by an arterial occlusion model."
  The document states this dataset was split at the participant level into training, validation, and held-out test sets. The exact number of participants or hours specifically in the training set is not separately quantified, but it is a substantial portion of the entire development dataset.

9. How Ground Truth for the Training Set was Established

The ground truth for the training set was established in the same manner as for the test set:

• For loss of pulse events, it was based on simulated events induced by an arterial occlusion model.
• For the absence of loss of pulse, it was based on free-living data from participants who did not experience such events, implying a ground truth established by the absence of a known medical condition/event.

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The Samsung ECG app with IHRN is an over-the-counter (OTC) software-only, mobile medical application operating on a compatible Samsung Galaxy Watch and Phone for informational use only in adults 22 years and older. The app analyzes pulse rate data to identify episodes of irregular heart rhythms suggestive of atrial fibrillation (AFib) and provides a notification suggesting the user record an ECG to analyze the heart rhythm. The Irregular Heart Rhythm Notification Feature 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 acquire pulse rate data when the user is still and analyze the data when determined sufficient toward surfacing a notification.

Following this prompt, or based on the user's own initiative, the app is intended to create, record, store, transfer, and display a single channel ECG, similar to a Lead I ECG. Classifiable traces are labeled by the app as sinus rhythm. AFib. high heart rate (non-AFib), or AFib with high heart rate with the intention of aiding heart rhythm identification.

The app is not intended for users with other known arrhythmias, and it is not intended to replace traditional methods of diagnosis or treatment. Users should not interpret or take clinical action based on the device output without consultation of a qualified healthcare professional.

The Samsung ECG App v1.3 is a software as a medical device (SaMD) that consists of a pair of mobile medical apps: one app on a compatible Samsung wearable and the other on a compatible Samsung phone, both general-purpose computing platforms.

When enabled, the wearable application of the SaMD uses a wearable photoplethysmography (PPG) sensor to background monitor cardiac signals from the user. The application examines beat-to-beat intervals and generates an irregular rhythm notification indicative of atrial fibrillation (AFib). Upon receiving an irregular rhythm notification or at their discretion, the user can record a single-lead ECG using the same wearable. The wearable application then calculates the average heart rate from the ECG recording and produces a rhythm classification. The wearable application also securely transmits the data to the ECG phone application on the paired phone. The phone application shows a time-stamped irregular rhythm notification history with heart rate information; ECG measurement history; and generates a PDF file of the ECG signal, which the user can share with their healthcare provider.

Acceptance Criteria and Device Performance for Samsung ECG App v1.3

1. Acceptance Criteria and Reported Device Performance

Note: The reported performance for Samsung ECG App v1.3's "Sinus rhythm (HR 50-150 BPM)" and "AFib (HR 50-150 BPM)" is presented with the same 95% CI: (94.0%, 97.8%). This might be a transcription error in the document, as specificity and sensitivity for different conditions would typically have distinct confidence intervals. Assuming independent calculations, these values are presented as they appear in the source.

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

• Sample Size: 1,013 subjects. These subjects contributed to 453 AFib recordings (heart rate 50 to 150 BPM) and 691 Sinus rhythm recordings (heart rate 50 to 150 BPM) for the primary endpoint analysis.
• Data Provenance: The study was a multi-center study, implying data from multiple locations, likely within the US given the FDA submission context and the racial demographics provided (predominantly Caucasian). The study was likely prospective as it involved recruiting subjects and collecting data for validation.

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

The document does not explicitly state the "number of experts" or their specific "qualifications" used to establish the ground truth for the test set. It mentions "Clinical Validation showing comparable clinical performance...compared to the reference device" and that the "ECG function accurately classified...compared against the standard Lead I ECG," implying that comparison was made to physician-adjudicated or expertly interpreted ECGs, but the details of the ground truth establishment are not provided.

4. Adjudication Method for the Test Set

The document does not explicitly state the adjudication method used for establishing the ground truth for the test set.

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

There is no mention of a Multi Reader Multi Case (MRMC) comparative effectiveness study being done, or any effect size of how much human readers improve with AI vs without AI assistance. The study focuses on the standalone performance of the device's ECG rhythm classification compared to a reference device.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

Yes, a standalone study was conducted. The "Clinical Validation" section details the performance of the "ECG rhythm classification of the Samsung ECG App v1.3" in terms of sensitivity and specificity against a clinical ground truth, without explicit human-in-the-loop interaction for the classification task itself. The device "accurately classified" recordings.

7. Type of Ground Truth Used

The ground truth used was clinical diagnosis based on "446 subjects diagnosed with AFib, 536 subjects without AFib, and 31 subjects diagnosed with another type of irregular rhythm." The performance was evaluated by comparing the device's classifications against "standard Lead I ECG" interpretation, implying expert consensus (from qualified healthcare professionals interpreting the standard ECGs) or clinical diagnosis as the ground truth.

8. Sample Size for the Training Set

The document does not specify the sample size for the training set. It focuses on the validation study.

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

The document does not provide information on how the ground truth for the training set was established.

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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 Samsung ECG Monitor Application with Irregular Heart Rhythm Notification is an over-the-counter (OTC) softwareonly, mobile medical application operating on a compatible Samsung Galaxy Watch and Phone for informational use only in adults 22 years and older. The app analyzes pulse rate data to identify episodes of irregular heart rhythms suggestive of atrial fibrillation (AFib) and provides a notification suggesting the user record an ECG to analyze the heart rhythm. The Irregular Heart Rhythm Notification Feature 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 acquire pulse rate data when the data when determined sufficient toward surfacing a notification.

Following this prompt, or based on the user's own initiative, the app is intended to create, record, store, transfer, and display a single-channel ECG. similar to a Lead I ECG. Classifiable traces are labeled by the app as either AFib or sinus rhythm with the intention of aiding heart rhythm identification.

The app is not intended for users with other known arrhythmias, and it is not intended to replace traditional methods of diagnosis or treatment. Users should not interpret or take clinical action based on the device of the of a qualified healthcare professional.

The Samsung ECG Monitor App with Irregular Heart Rhythm Notification (IHRN) Feature is a software as a medical device (SaMD) that consists of a pair of mobile medical apps: one app on a compatible Samsung wearable and the other on a compatible Samsung phone, both general-purpose computing platforms.

When enabled, the wearable application of the SaMD uses a wearable photoplethysmography (PPG) sensor to background monitor bio-photonic signals from the user. The application examines beat-to-beat intervals and generates an irregular rhythm notification indicative of atrial fibrillation (AFib). Upon receiving an irregular rhythm notification or at their discretion, the user can record a single-lead ECG using the same wearable. The wearable application then calculates the average heart rate from the ECG recording and produces a rhythm classification. The wearable application also securely transmits the data to the ECG phone application on the paired phone device. The phone application shows a time-stamped irregular rhythm notification history with heart rate information; ECG measurement history; and generates a PDF file of the ECG signal, which the user can share with their healthcare provider.

Here's a breakdown of the acceptance criteria and the study details for the Samsung ECG Monitor Application with Irregular Heart Rhythm Notification Feature, based on the provided text:

Acceptance Criteria and Device Performance

The document states that Samsung's algorithm performance for the IHRN function is substantially equivalent to the predicate device (Apple IRN Feature DEN180042) at both subject and tachogram levels, indicating these reported values met the acceptance criteria. For the ECG function, the device inherited the performance from the previously cleared Samsung ECG Monitor App (K201168) and thus the reported values were assumed to meet their prior acceptance criteria.

Study Details

2. Sample size used for the test set and the data provenance:

• IHRN Clinical Validation (PPG-based notification):

  • Analyzable Dataset for primary and secondary endpoints: 810 subjects (from 888 enrolled).
  • Tachogram-level assessment: 98 subjects with AFib episodes (over an hour) and 101 subjects with less than an hour of AFib or no AFib were randomly selected from the cardiologist-reviewed subjects. Up to 25 positive tachograms with reference ECG data were randomly selected from these subjects.
  • Data Provenance: The document does not explicitly state the country of origin, but it is a clinical study. The phrasing "All recruited subjects were at risk for AFib and had experienced symptoms..." suggests prospective data collection.

• ECG Function (on-demand):

  • No new clinical, human factors, or ECG database tests were conducted as the function was unchanged from the K201168 clearance. Therefore, a new test set was not used for this specific clearance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• IHRN Clinical Validation:
  • Subject-level ground truth: "clinician-adjudicated and cardiologist-reviewed patch ECG data." The exact number of clinicians/cardiologists for this overarching adjudication is not specified, but it implies multiple experts.
  • Tachogram-level ground truth: "Two board-certified cardiologists reviewed each reference ECG for annotation with a third cardiologist serving as tie-breaker."
  • Qualifications: "Board-certified cardiologists."

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

• Tachogram-level ground truth: 2+1 (Two board-certified cardiologists reviewed, with a third serving as a tie-breaker).
• Subject-level ground truth: Not explicitly stated as a specific numerical method (e.g., 2+1), but referred to as "clinician-adjudicated and cardiologist-reviewed," implying a consensus or expert-driven process.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• No MRMC comparative effectiveness study involving human readers with and without AI assistance was mentioned or conducted. The study evaluated the device's performance (IHRN feature) against a clinical ground truth, not the improvement of human readers using the device.

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

• Yes, the clinical validation study for the Irregular Heart Rhythm Notification (IHRN) feature primarily assesses the standalone performance of the PPG-based algorithm in identifying irregular rhythms and generating notifications. The "subject-level irregular rhythm notification accuracy" and "tachogram-level positive predictive value" are metrics of the algorithm's performance without direct human interpretation being part of the primary output.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

• IHRN Clinical Validation: Expert consensus using reference ECG patch data reviewed and adjudicated by clinicians and board-certified cardiologists.

8. The sample size for the training set:

• The document does not specify the sample size for the training set. It focuses on the validation study.

9. How the ground truth for the training set was established:

• The document does not specify how the ground truth for the training set (if any) was established. It only details the ground truth establishment for the test/validation set.

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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 Gili Pro BioSensor (Gili BioSensor System) includes an optical module that is intended to capture motion-vibration signals from an illuminated surface for assessment of physiological information. Such information, captured during spot-measurement, includes:

• Heart rate ●
• Respiratory rate .

The device is indicated for use by or under the supervision of healthcare professionals for adult patients in a hospital, outpatient, or other medical care settings, or for clinical research purposes.

The device should be used while the subject is seated upright either in a chair or in a bed. The information stored on the system may be reviewed by qualified persons.

The Gili Pro BioSensor is an optical system consisting of a sensing unit and a mobile unit with a preinstalled software application. The optical sensing unit is an aluminum enclosure which houses a lithium ion battery, illuminating optics (laser projector and laser pointer), digital image sensor, range meter, and firmware to facilitate data processing. The illuminating optics are based on eye-safe lasers which are compliant with Class I laser product accessible emission limits. The sensing unit is connected via a USB cable to a mobile unit on which the mobile application is installed.

The system illuminates the subject via a low-powered near infra-red (NIR at ~780 nm) light beam while an image sensor module captures the back reflected light pattern by the light sensor. The laser pointer illuminates in the visible spectrum (~650 nm) to facilitate proper positioning of the sensor relative to the subject. Changes in the reflected light pattern are coupled with motions of the illuminated surface, which are affected by heart and breathing motions. Analysis of these patterns through the software application correlate with heart and respiratory rates, as part of vital signs assessment. The heart and respiratory rate values are displayed on the user interface of the mobile unit.

Here's a detailed breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

Note: The specific numerical values for the acceptance criteria and confidence intervals are redacted in the provided document (indicated by "(b) (4)"). However, the document states that the device met these pre-specified criteria.

Study Details for Clinical Validation:

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

   • Sample Size: 120 subjects in the main, pivotal study. (An additional 10 subjects were in a pilot phase).
   • Data Provenance: The document does not specify the country of origin for the data. It indicates a "clinical study protocol and results" were provided by "ContinUse Biometrics Ltd. HaBarzel 32B Tel-Aviv, Israel," which suggests the study may have taken place in Israel, but this is not explicitly stated for the data collection itself. The study was prospective in nature, designed as a "two-phase trial."

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

   • Heart Rate Ground Truth: ECG (Electrocardiography) was used as the gold standard for determining heart rate and the presence of arrhythmia. This does not inherently involve human "experts" establishing ground truth in the same way an image interpretation might, but rather relies on objective physiological measurement.
   • Respiratory Rate Ground Truth: "Clinician over-scored capnography." This implies that clinicians (experts) were involved in interpreting or reviewing capnography data to establish ground truth. The specific number or qualifications of these clinicians are not provided.

3. Adjudication Method for the Test Set:

   • The document does not explicitly describe an adjudication method for the test set, such as a 2+1 or 3+1 consensus process. For heart rate, the ground truth was derived directly from ECG. For respiratory rate, "clinician over-scored capnography" was used, which suggests expert review, but the adjudication process (e.g., if multiple clinicians disagreed) is not detailed.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • The document does not mention an MRMC comparative effectiveness study involving human readers. The study focuses on the standalone performance of the Gili Pro BioSensor against reference devices. There is no information about human readers improving with or without AI assistance.

5. Standalone (Algorithm Only) Performance:

   • Yes, a standalone performance assessment was done. The entire clinical study described is dedicated to evaluating the algorithm's performance (via the Gili Pro BioSensor system) against gold-standard reference devices (ECG for HR, capnography for RR) without human interpretation of the Gili device's raw optical data being part of the primary outcome. The heart and respiratory rate values are "displayed on the user interface of the mobile unit," implying the algorithm generates these values autonomously.

6. Type of Ground Truth Used:

   • Heart Rate: Gold standard reference ECG (objective physiological measurement).
   • Respiratory Rate: Clinician over-scored capnography (a combination of objective physiological measurement and expert interpretation/scoring).

7. Sample Size for the Training Set:

   • The document does not provide information about the sample size used for the training set. The clinical study described is explicitly for validation (effectiveness) of the device performance.

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

   • As no information on a specific training set with ground truth is provided, this cannot be answered from the given text. The description focuses on the clinical validation study. It's common for development and training data to be separate from the robust clinical validation study.

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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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