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Eko Low Ejection Fraction Tool (ELEFT) is a software intended to aid clinicians in identifying individuals with Left Ventricular Ejection Fraction (LVEF) less than or equal to 40%. ELEFT takes as input ECG and heart sounds and is intended for use on patients at risk for heart failure. This population includes, but is not limited to, patients with: coronary artery disease; diabetes mellitus; cardiomyopathy; hypertension; and obesity.

The interpretations of heart sounds and ECG offered by the software are meant only to assist healthcare providers in assessing Left Ventricular Ejection Fraction ≤ 40% , who may use the result in conjunction with their own evaluation and clinical judgment. It is not a diagnosis or for monitoring of patients diagnosed with heart failure. This software is for use on adults (18 years and older).

Eko Low Ejection Fraction Tool (ELEFT) is an algorithm that is intended to aid clinicians to identify individuals with Left Ventricular Ejection Fraction (LVEF) less than or equal to 40%. ELEFT takes as input ECG and heart sounds from patients at risk for heart failure. The software uses signal processing as well as machine learning algorithms, to analyze the electrocardiogram (ECG) and heart sound/phonocardiogram (PCG) recording signals generated by FDA-cleared Eko Stethoscopes and saved as .WAV file recordings in the Eko Cloud. ELEFT is a machine learning based notification software which employs machine learning techniques to suggest the likelihood of LVEF

The Eko Low Ejection Fraction Tool (ELEFT) is a software intended to aid clinicians in identifying individuals with Left Ventricular Ejection Fraction (LVEF) less than or equal to 40%. The device takes ECG and heart sound inputs and processes them using signal processing and machine learning algorithms.

Here's an analysis of its acceptance criteria and the study proving its performance:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document doesn't explicitly state "acceptance criteria" in a numerical target format (e.g., "Sensitivity must be >= X%"). However, the clinical performance results presented demonstrate the device's capability to detect Low EF. The acceptance effectively hinges on the presented sensitivity and specificity values.

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

• Test Set Sample Size: 3,456 unique subjects. After excluding 307 recordings due to poor ECG quality, the performance analysis was based on the remaining suitable recordings.
• Data Provenance: Retrospective data collected from:
  • US, 5 sites: 2,960 patients.
  • India, 1 site: 496 patients.

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

• Number of Experts: Not explicitly stated as a number, but the ground truth for ejection fraction was "overread by a board-certified cardiologist." This implies at least one, and potentially multiple, board-certified cardiologists were involved in reviewing the echocardiogram results.
• Qualifications of Experts: Board-certified cardiologists.

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method like 2+1 or 3+1 for resolving discrepancies in ground truth establishment. It states that the "subject's true ejection fraction was measured by the echocardiogram machine's integrated cardiac quantification software at the echocardiogram and then overread by a board-certified cardiologist." This suggests a single expert review after automated measurement, with no mention of multiple reviewers or a formal reconciliation process if initial measurements or interpretations differed.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not conducted. The study focuses solely on the standalone performance of the ELEFT algorithm without a human-in-the-loop component or evaluating the improvement of human readers with AI assistance.

6. Standalone (Algorithm Only) Performance

Yes, a standalone (algorithm only) performance study was conducted. The results for sensitivity, specificity, PPV, and NPV presented in Table 2 and the subsequent text (page 9) are for the ELEFT algorithm's performance in differentiating between Low EF (≤40%) and Normal EF (>40%).

7. Type of Ground Truth Used

The type of ground truth used was expert consensus / pathology based on instrumental measurements and expert review:

• Echocardiogram (Instrumental Measurement): The true ejection fraction was measured by the echocardiogram machine's integrated cardiac quantification software.
• Expert Overread: This measurement was "overread by a board-certified cardiologist."
• Categorization: Ejection status (Low EF or Normal EF) was then assigned based on these measured and reviewed values.

8. Sample Size for the Training Set

The sample size for the training set was 1,852 patients. This data was contributed from:

• US, 7 sites: 1,515 patients.
• India, 1 site: 337 patients.

9. How Ground Truth for the Training Set Was Established

The document does not explicitly detail the exact process for establishing ground truth for the training set. However, given the consistency in the data description and the validation methodology, it is highly probable that the ground truth for the training set was established using the same methodology as the test set: gold standard echocardiogram measurements, subsequently overread by board-certified cardiologists, and then categorized into Low EF (≤40%) or Normal EF (>40%).

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The CORE 500 Digital Stethoscope is intended to be used by clinicians to electronically amplify, filter, and transfer body sounds and three lead electrocardiogram (ECG) waveforms. The CORE 500 Digital Stethoscope also displays ECG waveforms and heart rate on the display and accompanying mobile application (when prescribed or used under the care of a clinician).

The data offered by the device is only significant when used in conjunction with clinician evaluation as well as consideration of other relevant patient data.

CORE 500 Digital Stethoscope (CORE 500) is an electronic stethoscope with integrated electrodes for electrocardiogram (ECG). The device consists of a chestpiece, detachable earpiece (Eko Earpiece) and a mobile application (Eko App) and is intended as a digital auscultation tool on patients requiring physical assessment by the health care providers. CORE 500 provides the ability to amplify, filter, and transfer body sounds with the chestpiece diaphragm, and three lead ECG through electrodes integrated around the chestpiece.

CORE 500 features three auscultation modes for better auscultation experience by filtering acoustic data and enhancing the primary frequency range of particular body sounds: Cardiac Mode for heart sounds. Pulmonary Mode for lung sounds, and Wide Band Mode for general auscultation. CORE 500 also detects and computes the heart rate in real-time based on the phonocardiogram (PCG) data.

Here's a breakdown of the acceptance criteria and the study information based on the provided FDA 510(k) summary for the Eko CORE 500 Digital Stethoscope:

Note: The provided document primarily focuses on demonstrating substantial equivalence to a predicate device through non-clinical testing. It does not detail a clinical study with specific acceptance criteria related to diagnostic performance involving human subjects and ground truth established by experts. The "acceptance criteria" discussed below are based on the non-clinical performance data provided.

1. Table of Acceptance Criteria and Reported Device Performance

Given that this is a 510(k) for an electronic stethoscope, the "acceptance criteria" are derived from the performance data provided to show equivalence and functionality. The document doesn't explicitly list pass/fail criteria with numerical thresholds in the same way a clinical trial might, but it states that "the CORE 500 Digital Stethoscope complies with" or "demonstrated compliance" with various standards and performance benchmarks.

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

The provided document describes non-clinical bench testing rather than a clinical study with a "test set" of patient data. Therefore, there is no patient sample size or provenance information in the sense of a clinical trial (e.g., country of origin, retrospective/prospective). The testing involved physical devices and simulated or controlled environments to assess performance properties.

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

As this was non-clinical bench testing, no medical experts were explicitly used to establish "ground truth" for a patient test set. The tests focused on objective electrical, mechanical, and software performance criteria verified against technical standards and internal specifications, not diagnostic accuracy in a clinical context.

4. Adjudication Method for the Test Set

Since there was no patient test set requiring expert interpretation or diagnosis, there was no adjudication method (like 2+1 or 3+1) used.

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

No, an MRMC comparative effectiveness study was not done. The document describes non-clinical performance data for substantial equivalence, not a study assessing how human readers improve with or without AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

The document pertains to the CORE 500 Digital Stethoscope hardware, which amplifies, filters, and transfers body sounds and ECG waveforms, and displays ECG waveforms and heart rate. While the device processes signals, it's a diagnostic tool, and the indications for use explicitly state: "The data offered by the device is only significant when used in conjunction with clinician evaluation as well as consideration of other relevant patient data." This indicates that the device is intended for human-in-the-loop use. Therefore, a standalone algorithm-only performance assessment in a diagnostic context was not the focus of this submission. The "heart rate detection" is a standalone function of the device, but its diagnostic interpretation is with a clinician.

7. The Type of Ground Truth Used

For the non-clinical tests described:

• Biocompatibility: Ground truth is established by adherence to ISO 10993-1:2018 standards and laboratory testing results.
• Electrical Safety & EMC: Ground truth is established by compliance with IEC 60601-1 and IEC 60601-1-2 standards.
• Software V&V: Ground truth is established by meeting FDA Guidance for Premarket Submissions for Software and internal software requirements.
• Bench Testing (Audio, Electrical/Mechanical, Heart Rate): Ground truth is based on engineering specifications, established physical principles, and comparison to calibrated reference instruments/signals.

8. The Sample Size for the Training Set

The document describes premarket notification for a hardware device (digital stethoscope) with integrated capabilities. It does not mention machine learning or AI algorithms requiring a "training set" in the context of diagnostic interpretation (e.g., for automated murmur detection or arrhythmia classification). While heart rate detection is mentioned, the details of its underlying algorithm training are not provided. No specific "training set" size is part of this 510(k) summary.

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

Since no explicit "training set" for a diagnostic AI algorithm is described as part of this submission, the method for establishing its ground truth is not applicable here.

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