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The AI-ECG Tracker is intended to be used by qualified healthcare professionals in hospitals and healthcare facilities for the assessment of arrhythmias using ECG data acquired from adults (age 22 and older) without pacemakers. The product supports downloading and analyzing data recorded from electrodes with conductive paste/gel placed on standard location in compatible formats from any device used for the arrhythmia diagnostics such as Holter, event recorder, 12-lead ambulatory ECG devices when assessment of the rhythm is necessary. The AI-ECG Tracker provides ECG signal processing and analysis on a beat by beat basis, QRS detection, Supraventricular Ectopic Beat detection, QRS feature extraction, interval measurement, and rhythm analysis. The AI-ECG Tracker is not for use in life supporting or sustaining systems or ECG monitoring and Alarm devices.

The AI-ECG Tracker interpretation results are not intended to be the sole means of diagnosis for any abnormal ECG. It is offered to physicians and clinicians on an advisory basis only in conjunction with the physician's knowledge of ECG patterns, patient background, clinical history, symptoms, and other diagnostic information.

The AI-ECG Tracker is a distributed ECG auto analysis system designed to assist physicians and qualified healthcare professionals in measuring and interpreting ambulatory ECG data. The interpretations by the analysis program can be confirmed, edited, modified, or deleted by the physician and qualified healthcare professionals. The program is intended for use by qualified healthcare professionals in hospitals and other healthcare facilities for the assessments of common cardiac arrhythmias using ECG data acquired from adults (age 22 and older) without pacemakers.

The AI-ECG Tracker receives ECG waveform data uploaded by a user, analyzes ECG data and automatically interprets on a computer server. The ECG measurement, interpretation and waveform data are then downloaded to the Physician Diagnostic Client for a physician to review, modify, confirm the analysis statements, and print the report. The original ECG waveform data is stored permanently in the user's server computer securely.

The system uses a machine learning based process (convolutional neural network or CNN) only for development of the AI ECG algorithm. The AI ECG algorithm is only used for beat classification. After the AI ECG algorithm is developed, the AI-based beat classification model is locked in the released product which means the marketed device doesn't have active machine learning or selflearning features.

Here's a breakdown of the acceptance criteria and study information based on the provided document:

1. Acceptance Criteria and Reported Device Performance

The document primarily focuses on demonstrating substantial equivalence to a predicate device rather than explicitly stating acceptance criteria with specific performance metrics for the AI-ECG Tracker. However, it does reference compliance with performance standards.

Note: The document states that "Bench tests were conducted to verify that the subject device met all design specifications, as was Substantially Equivalent (SE) to the predicate device." This suggests that the device's performance was evaluated against the predicate device's capabilities and relevant standards to establish substantial equivalence. Specific quantitative performance values (e.g., sensitivity, specificity for arrhythmia detection) are not provided in this document.

2. Sample Size for Test Set and Data Provenance

The document does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective nature of the data). It mentions "bench tests," which typically cover technical performance and compliance with standards rather than clinical validation on a specific dataset.

3. Number of Experts and Qualifications for Ground Truth

The document does not provide details on the number of experts used or their qualifications for establishing ground truth for the test set.

4. Adjudication Method for the Test Set

The document does not specify any adjudication method (e.g., 2+1, 3+1, none) used for the test set.

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

There is no indication that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was conducted or reported in this document. The focus is on the device's standalone performance and its equivalence to a predicate device.

6. Standalone (Algorithm Only) Performance Study

Yes, a standalone (algorithm only without human-in-the-loop performance) study was performed. The document states:

• "The AI-ECG Tracker provides ECG signal processing and analysis on a beat by beat basis, QRS detection, Supraventricular and Ventricular Ectopic Beat detection, QRS feature extraction, interval measurement, heart rate measurement, and rhythm analysis."
• "Bench tests were conducted to verify that the subject device met all design specifications..."
• The "AI-ECG Tracker" is presented as an automated analysis system that "automatically interprets on a computer server."

This indicates that the algorithm's performance in analyzing ECG data was evaluated independently.

7. Type of Ground Truth Used

The specific type of ground truth used for performance evaluation is not explicitly stated. However, given the context of "bench tests" and compliance with standards like AAMI/ANSI EC57, it is highly likely that the ground truth for algorithm performance was established through:

• Reference annotated ECG databases: Standardized ECG databases with expert-adjudicated annotations are commonly used for validating ECG analysis algorithms.
• Expert consensus: For specific cases or discrepancies, expert cardiologists would establish the true rhythm or beat classification.

8. Sample Size for the Training Set

The document does not provide the sample size used for the training set. It mentions that the "system uses a machine learning based process (convolutional neural network or CNN) only for development of the AI ECG algorithm."

9. How Ground Truth for the Training Set Was Established

While the document states that a CNN was used for "development of the AI ECG algorithm" (for beat classification), it does not explicitly detail how the ground truth for this training set was established. In typical machine learning development for medical devices, this would involve:

• Expert annotation: Cardiologists or trained technicians meticulously annotating ECG recordings to label various beat types and rhythms.
• Validated databases: Leveraging existing, publicly available, or proprietary ECG databases that come with expert-verified annotations.

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Electrocardiographs, ECG-1101G(1), ECG-1103G(1), ECG-1103LW(1), ECG-1106L, ECG-1112 and ECG-1112D, are intended to acquire ECG signals from adult patients through body surface ECG electrodes. It could complete the ECG measurements of QRS detection. Heart Rate, ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB) and ST Segment Deviation. ECG with measurements is offered to clinician on an advisory basis only. Digital Electrocardiographs shall be used in healthcare facilities by doctors and/or trained healthcare professionals.

Carewell Digital Electrocardiographs, ECG-1101G(I) / ECG-1103G(I)/ ECG-1103LW(I) / ECG-1106L /ECG-1112 / ECG-1112D, are designed to acquire, analyze, display and record ECG signals from patient body surface by ECG electrodes. After been amplified, fittered and analyzed, the ECG signal waveforms and analysis results are displayed on the LCD and recorded on the paper through thermal printer. ECG data, result and patient information could be stored in the memory of the device.

All the models, ECG-1101G(1) / ECG-1103G(1) ECG-1103LW(1) / ECG-1106L /ECG-1112 / ECG-1112D of the proposed device, Carewell Digital Electrocardiographs, have difference in appearance. but follow the same design principle and similar technical specifications:

They consist of three modules, which are power module and control module, and they have three operation modes, including AUTO Mode (automatic mode), MAN Mode (manual mode) and ANA Mode (analysis mode).

They are standard twelve leads, including bipolar limb leads, augmented unipolar limb leads and unipolar chest leads. And they are using the same accessories.

Here's a breakdown of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document references adherence to several standards but does not explicitly state quantitative acceptance criteria or detailed performance results in terms of accuracy or specific metrics for QRS detection, Heart Rate, VEB, SVEB, or ST Segment Deviation. The statement "Bench tests were conducted to verify that the proposed device met all design specifications as was Substantially Equivalent (SE) to the predicate device" is a general claim rather than a detailed performance report.

However, the "Intended Use Statement" lists the capabilities that the device is designed to perform, which implies expected performance for these metrics. Based on the mention of ANSI/AAMI EC57:2003, Testing and Reporting performance results of cardiac rhythm and ST-segment measurement algorithms, it can be inferred that the device was tested against the performance requirements outlined in this standard. Without direct access to the full test report or the specific criteria within EC57 for this submission, we can only list the intended functionalities as the implied performance areas.

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

The provided 510(k) summary does not specify the sample size for the test set or the data provenance (e.g., country of origin, retrospective or prospective) for the performance evaluation of the ECG measurement algorithms (QRS, HR, VEB, SVEB, ST Segment). It only mentions "Bench tests were conducted to verify that the proposed device met all design specifications."

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

The document does not provide any information regarding the number of experts used, their qualifications, or how ground truth was established for the test set.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method for the test set.

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

A multi-reader multi-case (MRMC) comparative effectiveness study was not mentioned or performed according to the provided text. The submission focuses on device equivalence and performance against standards, not on human reader improvement with AI assistance.

6. Standalone Performance Study

Yes, a standalone study was indicated. The document states, "Bench tests were conducted to verify that the proposed device met all design specifications." The purpose of these tests was to demonstrate that the device's algorithms (for QRS detection, Heart Rate, VEB, SVEB, and ST Segment Deviation) performed according to established standards (specifically referencing ANSI/AAMI EC57:2003), enabling the device to be considered "Substantially Equivalent" to a predicate device. This implies testing of the algorithm's performance independent of human-in-the-loop interaction.

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used for the performance evaluation of the algorithms. However, given the reference to ANSI/AAMI EC57:2003, Testing and Reporting performance results of cardiac rhythm and ST-segment measurement algorithms, it is highly probable that a standard, meticulously annotated ECG database (potentially with expert consensus annotations or derived from clinical events) would have been used to establish the ground truth for evaluating the algorithm's performance against the specified metrics.

8. Sample Size for the Training Set

The document does not provide any information regarding the sample size for the training set.

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

The document does not provide any information on how the ground truth for the training set was established, as no details about a training set are mentioned.

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