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510(k) Data Aggregation

    K Number
    K201525
    Device Name
    ECG App
    Manufacturer
    Apple Inc.
    Date Cleared
    2020-10-08

    (122 days)

    Product Code
    QDA
    Regulation Number
    870.2345
    Type
    Traditional
    Panel
    Cardiovascular
    Reference & Predicate Devices
    DEN180044
    Predicate For
    K243236,K240795
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The ECG app is a software-only mobile medical application intended for use with the Apple Watch to create, record, store, transfer, and display a single channel electrocardiogram (ECG) similar to a Lead I ECG. The ECG app determines the presence of atrial fibrillation (AFib), sinus rhythm, and high heart rate (no detected AF with heart rate 100-150 bpm) on a classifiable waveform. The ECG app is not recommended for users with other known arrhythmias.

    The ECG app is intended for over-the-counter (OTC) use. The ECG data displayed by the ECG app is intended for informational use only. The user is not intended to interpret or take clinical action based on the device output without consultation of a qualified healthcare professional. The ECG waveform is meant to supplement rhythm classification for the purposes of discriminating AFib from sinus rhythm and is not intended to replace traditional methods of diagnosis or treatment.

    The ECG app is not intended for use by people under 22 years old.

    Device Description

    The ECG 2.0 app comprises a pair of mobile medical apps - one on Apple Watch and the other on the iPhone.

    The ECG Watch app analyzes data collected by the integrated electrical sensors on a compatible Apple Watch to generate an ECG waveform similar to a Lead I. calculate average heart rate, and provide a rhythm classification to the user for a given 30 second session. When a user opens the ECG Watch app while wearing the Watch on one wrist, and places the finger of the opposite hand on the digital crown, they are completing the circuit across the heart which begins a recording session.

    Once the recording session is complete, the ECG Watch app performs signal processing, feature extraction and rhythm classification to generate a session result.

    The resulting classification and average heart rate for the session, along with educational information, will be displayed to the user within the ECG Watch app.

    The ECG iPhone app contains the on-boarding and educational materials that a user must review prior to taking an ECG reading. The ECG iPhone app is included in the Health App, which allows users to store, manage, and share health and fitness data, and comes pre-installed on every iPhone. The ECG 2.0 app expands the classifiable heart range, introduces new classification results, and introduces minor, non-userfacing algorithm updates. These changes will be reflected in both the Apple Watch app, and also on the corresponding iPhone app within the Health App.

    AI/ML Overview

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

    1. Table of acceptance criteria and the reported device performance

    Acceptance CriteriaDevice Performance (ECG 2.0 App)
    AFib Classification Sensitivity (HR 50-150 bpm)98.5%
    Sinus Rhythm Classification Specificity (HR 50-150 bpm)99.3%
    PQRST Waveform Visual Acceptability100% pass rating
    R-wave Amplitude Assessment97.2% total pass rating

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

    • Sample size: Approximately 546 subjects.
      • 305 subjects were in the Atrial Fibrillation cohort.
      • 241 subjects were in the normal sinus rhythm cohort.
    • Data provenance: Prospective, multi-center clinical trial. The country of origin is not explicitly stated, but it is a "multi-center" trial, implying diverse participant recruitment.

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

    • Number of experts: Not explicitly stated, but the ground truth was established by "a cardiologist." This implies at least one, likely a panel or multiple, to ensure robustness, though the exact number isn't quantified.
    • Qualifications of experts: "Cardiologist." Years of experience are not specified.

    4. Adjudication method for the test set

    • The text states: "Rhythm classification of a 12-lead ECG by a cardiologist was compared to the rhythm classification of a simultaneously collected ECG from the ECG 2.0 app." This indicates that the cardiologist's interpretation of a 12-lead ECG served as the ground truth. It does not explicitly describe an adjudication method like 2+1 or 3+1 if multiple cardiologists were involved. It implies a single definitive classification by the cardiologist.

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

    • No, a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with AI assistance versus without AI assistance was not conducted or reported in the provided text. The study focused on the standalone performance of the ECG 2.0 app against a cardiologist's interpretation.

    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 performance metrics (sensitivity, specificity, waveform acceptability) reflect the algorithm's direct classification capabilities compared to the ground truth established by a cardiologist. The device is intended for over-the-counter use, and its performance in classifying AFib and sinus rhythm was assessed directly.

    7. The type of ground truth used

    • The ground truth used was expert consensus / diagnosis from a cardiologist's interpretation of a 12-lead ECG.

    8. The sample size for the training set

    • The document does not explicitly state the sample size for the training set. It only mentions the test set (clinical trial of 546 subjects).

    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 primarily focuses on the validation study. However, given that it states "Apple conducted database testing using a previously adjudicated dataset" for "ECG Database Testing per EC57," it is highly probable that the training data's ground truth was also established by expert cardiologists adjudicating ECGs in a similar manner to the test set, but this is not explicitly detailed for the training set.
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    K Number
    DEN180044
    Device Name
    ECG App
    Manufacturer
    Apple Inc
    Date Cleared
    2018-09-11

    (28 days)

    Product Code
    QDA,ODA
    Regulation Number
    870.2345
    Type
    Direct
    Panel
    Cardiovascular
    Reference & Predicate Devices
    N/A
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The ECG app is a software-only mobile medical application intended for use with the Apple Watch to create, record, store, transfer, and display a single channel electrocardiogram (ECG) similar to a Lead I ECG. The ECG app determines the presence of atrial fibrillation (AFib) or sinus rhythm on a classifiable waveform. The ECG app is not recommended for users with other known arrhythmias.

    The ECG app is intended for over-the-counter (OTC) use. The ECG data displayed by the ECG app is intended for informational use only. The user is not intended to interpret or take clinical action based on the device output without consultation of a qualified healthcare professional. The ECG waveform is meant to supplement rhythm classification for the purposes of discriminating AFib from normal sinus rhythm and not intended to replace traditional methods of diagnosis or treatment.

    The ECG app is not intended for use by people under 22 years old.

    Device Description

    The device (ECG App) comprises a pair of mobile medical apps — one on Apple Watch (the Watch App) and the other on the iPhone (iPhone App) - intended to record, store, transfer, and display a single lead ECG signal similar to a lead I. The ECG Watch App is intended to analyze this single lead data and detect the presence of atrial fibrillation (referred into this document as AFib or AF) and sinus rhythm in adults. It is also intended to acquire and analyze the single lead ECG recordings for display on the iPhone. The ECG iPhone App is included in the Health App, which is intended to store, manage, and share health and fitness data, and comes pre-installed on every iPhone.

    AI/ML Overview

    Here's a breakdown of the acceptance criteria and the study that proves the device (ECG App) meets these criteria, based on the provided text:

    Acceptance Criteria and Device Performance

    Acceptance Criteria (Performance Goals)Reported Device Performance
    Primary Endpoint: Sensitivity of the ECG App algorithm in detecting AF compared with physician-adjudicated 12-lead ECG. Goal: ≥ 90% sensitivity.98.3% sensitivity (97.5% LCB: 95.8%) for AF detection among classifiable recordings. This meets the 90% goal.
    Primary Endpoint: Specificity of the ECG App algorithm in detecting AF compared with physician-adjudicated 12-lead ECG. Goal: ≥ 92% specificity.99.6% specificity (97.5% LCB: 97.7%) for AF detection among classifiable recordings. This meets the 92% goal.
    Secondary Endpoint 1: Qualitative assessment: The proportion of paired ECG strips appear to overlay to the unaided eye. Goal: > 0.80 (80%)99.2% of subjects (125/126) had an ECG App waveform that was considered clinically equivalent to the gold standard based on qualitative assessment. This meets the 80% goal.
    Secondary Endpoint 2: Quantitative assessment: The proportion of paired R-wave amplitude measurements within 2 mm of each other. Goal: > 0.80 (80%)97.6% of subjects (123/126) had a paired R-Wave amplitude difference ≤ 2 mm. This meets the 80% goal.
    Special Control 1.a: Ability to obtain an ECG of sufficient quality for display and analysis.Demonstrated through electromagnetic compatibility, electrical safety, and signal acquisition assessments (IEC standards), and confirmed by the high rates of classifiable recordings in the clinical study. Also evidenced by the waveform assessment results (99.2% qualitative equivalence, 97.6% R-wave agreement).
    Special Control 1.b: Performance characteristics of the detection algorithm as reported by sensitivity and either specificity or positive predictive value.Met by the primary endpoint results for sensitivity (98.3%) and specificity (99.6%).
    Special Control 2: Software verification, validation, and hazard analysis.Documentation indicates all elements for "Moderate" level of concern software, including V&V testing, hazard analysis, cybersecurity, etc., were performed.
    Special Control 3: Non-clinical performance testing validated detection algorithm performance using a previously adjudicated data set."ECG Database Testing" was conducted using adjudicated AHA and MIT databases, with "The database annotations used as ground truth." Specific results are redacted but the testing itself was performed.
    Special Control 4.a: Human factors and usability testing: The user can correctly use the device based solely on reading the device labeling.A Human Factors Validation Study was performed with 50 participants across three user groups to assess usability and critical tasks. The study assessed completion and success criteria.
    Special Control 4.b: Human factors and usability testing: The user can correctly interpret the device output and understand when to seek medical care.Assessed during the Human Factors Validation Study, which focused on whether users understood output and limitations, and if they failed to seek care when needed.
    FDA's Probable Benefits Outweigh Probable Risks ConclusionAchieved, leading to the De Novo grant. The study demonstrated high accuracy and minimal safety concerns.

    Study Details

    1. Sample Size and Data Provenance:

      • Clinical Study Test Set Sample Size: 602 total subjects enrolled. After exclusions, 588 eligible subjects were used for analysis (AF Cohort: 301, SR Cohort: 287). For the "Classifiable Analysis Set" (where the algorithm output a diagnosis), 556 subjects were included (488 had a diagnosis).
      • Data Provenance: Not explicitly stated regarding country of origin, but it was a "multi-center" study, implying multiple sites within a geographic region (likely the US, given FDA submission). The study was prospective.
      • Waveform Assessment Analysis Set Sample Size: 139 subjects initially selected, with 126 subjects remaining for analysis after exclusions (60 AF, 65 SR, 99.2%).
      • ECG Database Testing Sample Size: "(b) (4)" records from adjudicated AHA and MIT databases, split into "(b) (4)" 30-second segments. Specific numbers are redacted.

    2. Number of Experts and Qualifications for Clinical Study Ground Truth:

      • Number of Experts: Three (3) blinded independent board-certified cardiologists.
      • Qualifications: "board-certified cardiologists." No specific years of experience are listed.

    3. Adjudication Method for Clinical Study Test Set Ground Truth:

      • Adjudication Method: "If the readers disagreed on the diagnosis, the final interpretation was determined by the simple majority rule." This is a 3-reader majority rule method.

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

      • The provided text does not describe an MRMC comparative effectiveness study where human readers' performance with and without AI assistance is compared.
      • The study primarily focuses on the standalone performance of the AI algorithm (ECG App) against physician-adjudicated ground truth.
      • There was an "Additional Analysis" involving physician interpretation of ECG App strips compared to 12-lead ECGs, but this was to evaluate the quality of the ECG App recording and not a comparative study of human performance with/without AI assistance.

    5. Standalone Performance:

      • Yes, a standalone (algorithm only without human-in-the loop performance) evaluation was done as the primary endpoint of the clinical study. The ECG App algorithm's classification was directly compared against the physician-adjudicated 12-lead ECG ground truth.

    6. Type of Ground Truth Used:

      • Clinical Study: Expert Consensus (three blinded independent board-certified cardiologists reviewing 12-lead ECG recordings with majority rule).
      • ECG Database Testing: "The database annotations were used as ground truth." These are typically expert-adjudicated public datasets like AHA and MIT databases.

    7. Sample Size for Training Set:

      • The provided text does not specify the sample size for the training set of the ECG App algorithm. It only details the test sets used for validation.

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

      • The text does not provide information on how the ground truth for the training set was established. It only mentions the ground truth methodology for the test sets (clinical study and database testing).

    This detailed analysis covers the requested information based on the provided FDA De Novo submission text.

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