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The Eko Foundation Analysis Software with Transformers (EFAST) is intended to support healthcare professionals in the evaluation of patients' heart sounds and electrocardiograms (ECGs). The software calculates heart rate, detects the presence of murmurs associated with Structural Heart Disease, and determines murmur timing including the timing of S1, S2 heart sounds. When ECG is available, the software detects the presence of atrial fibrillation and normal sinus rhythm.

The software is intended for use by or under the supervision of a healthcare professional and is not to be used as a sole means of diagnosis. The interpretations of heart sounds and ECG offered by the software are designed to support and not replace the healthcare professional's clinical judgment. The murmur detection is intended for use on both pediatric and adult patients, while the detection of atrial fibrillation is intended for use on adults (> 18 years).

Eko Foundation Analysis Software with Transformers (EFAST) is a cloud-based Software as a Medical Device (SaMD) intended to provide clinical decision support to healthcare professionals (HCP) in the evaluation of patients' heart sounds (phonocardiogram, PCG) and electrocardiograms (ECGs). The software employs signal processing techniques and machine learning (Deep Neural Networks) to perform simultaneous analysis of recorded heart sounds and ECG data (when available), and identify the presence of murmurs associated with Structural Heart Disease, and determine murmur timing including the timing of S1, S2 heart sounds. When ECG is available, the software also detects the presence of atrial fibrillation and sinus rhythm.
The software does not identify other arrhythmias. In addition, it calculates the heart rate of the patient.

The EFAST Software accepts input consisting of heart sounds and ECG waveforms recorded using Eko Digital Stethoscopes that are saved in .WAV file format and stored in the Eko Cloud, which utilizes the Amazon Web Services (AWS) Simple Storage Service (S3) for data storage and for easy communication with the EFAST analysis server. When an API Request is sent to the EFAST API, the corresponding recordings (PCG and/or ECG) are accessed by the EFAST software and they are analyzed by the EFAST software components. After analysis, the EFAST software returns a JSON format data structure with the algorithm results, which is saved back to the Eko Cloud and can be displayed on EFAST API integrated target software or devices (e.g. user-facing apps).

The EFAST Software consists of the following algorithm components:

• Heart Sound Signal Quality Classifier: Evaluates whether an audio recording contains heart sounds of sufficient quality for further clinical analysis
• Structural Murmur Classifier: Determines whether a good-quality heart sound recording contains a structural murmur or not
• Heart Sound Timing & Murmur Timing: Determines the timing of S1 and S2 heart sounds and determines whether a structural murmur occurs during the systolic or diastolic phase
• ECG Rhythm Classifier: Analyzes ECG signals and categorizes them into one of four classifications: Sinus Rhythm, Atrial Fibrillation, Unspecified Rhythm, and Poor ECG Signal
• Heart Rate Calculation: Signal processing algorithm that utilizes ECG and PCG to calculate heart rate value in beats per minute

Here's a detailed breakdown of the acceptance criteria and the studies proving the device's performance, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Reported Device Performance

Note on Acceptance Criteria: The document does not explicitly state numerical "acceptance criteria" but rather presents the device's performance results in comparison to a predicate device, implying that the demonstrated performance is considered acceptable for substantial equivalence. The predicate device's performance often sets an implicit benchmark.

Study Details: Structural Murmur Classification

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

• Test set sample size: 2,460 unique heart sound recordings (from 615 unique subjects). 259 recordings were excluded due to lack of cardiologist agreement.
• Data provenance: Retrospective study of de-identified data. Three sites within the US (2 sites) and Canada (1 site).

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

• Number of experts: "Multiple cardiologists" annotated the recordings.
• Qualifications: "Cardiologists." No further specific qualifications (e.g., years of experience) are provided in this document.

4. Adjudication method for the test set:

• For 2,460 initial recordings, "multiple cardiologists" annotated them.
• For 259 of these recordings, the cardiologists "could not come to agreement," leading to their exclusion from the analysis. This implies some form of consensus or agreement-based adjudication, where a lack of agreement led to exclusion rather than a specific tie-breaking rule (e.g., 2+1, 3+1).

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 vs. without AI assistance is described in this section for structural murmur classification. The comparison is between the EFAST algorithm's standalone performance and the predicate device's standalone performance ("Algorithm Performance Comparison to Predicate").

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

• Yes, the presented performance metrics (Sensitivity, Specificity) are for the EFAST algorithm operating in a standalone capacity ("EFAST Structural Murmur Classification Performance" and "Algorithm Performance Comparison to Predicate").

7. The type of ground truth used:

• Ground truth was established by "pairing the cardiologist annotations with gold standard echocardiogram." This indicates a hybrid ground truth, combining expert interpretation with objective clinical imaging (echocardiogram).

8. The sample size for the training set:

• Not specified in this document. The document only mentions validation on "proprietary datasets."

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

• Not specified in this document.

Study Details: Heart Sound Timing (S1/S2 Detection)

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

• Test set sample size: 96 heart sound recordings.
• Data provenance: Retrospective study. Data collected using Eko CORE or Eko CORE 500 Digital Stethoscopes. Country of origin not explicitly stated for this subset, but the broader structural murmur study involved US and Canada.

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

• Number of experts: "Expert cardiologists."
• Qualifications: "Cardiologists." No further specific qualifications are provided.

4. Adjudication method for the test set:

• Not explicitly stated, but "annotated by expert cardiologists" implies expert consensus for these 96 recordings, as no exclusions due to disagreement are mentioned. "Cardiologists were blinded to all subject demographic data and echocardiogram findings during annotation."

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 described.

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

• Yes, the presented performance is for the EFAST algorithm in a standalone capacity ("EFAST Heart Sound Timing Performance").

7. The type of ground truth used:

• Expert cardiologist annotations of "timing of the S1 and S2 sounds."

8. The sample size for the training set:

• Not specified.

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

• Not specified.

Study Details: ECG Rhythm Classification (Atrial Fibrillation Detection)

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

• Test set sample size: 1,256 ECG recordings (from 314 unique subjects). 97 recordings were excluded due to lack of expert agreement.
• Data provenance: Retrospective study of de-identified data. Two sites within the US.

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

• Number of experts: "Multiple experts."
• Qualifications: "Electrophysiologists, certified cardiographic technicians." No further specific qualifications (e.g., years of experience) are provided.

4. Adjudication method for the test set:

• "Multiple experts" annotated the recordings.
• For 97 recordings, "the experts did not come to an agreement," leading to their exclusion. Similar to the structural murmur study, this indicates an agreement-based adjudication where disagreement led to exclusion.

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 vs. without AI assistance is described. The comparison is between the EFAST algorithm's standalone performance and the predicate device's standalone performance ("Algorithm Performance Comparison to Predicate").

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

• Yes, the presented performance metrics (Overall Sensitivity, Overall Specificity) are for the EFAST algorithm operating in a standalone capacity ("EFAST Atrial Fibrillation Detection Performance" and "Algorithm Performance Comparison to Predicate").

7. The type of ground truth used:

• Expert annotations related to "quality and the presence of atrial fibrillation."

8. The sample size for the training set:

• Not specified.

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

• Not specified.

Study Details: Heart Rate Calculation

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

• Not explicitly stated, but the performance is presented as a summary ("found to be less than 5%").

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

• Ground truth method not explicitly detailed in the summary, but typically for heart rate, it involves reference devices or manual counting from a known good signal.

4. Adjudication method for the test set:

• Not explicitly stated.

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 described.

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

• Yes, the performance is for the algorithm itself.

7. The type of ground truth used:

• Implied to be a reference standard for heart rate measurement.

8. The sample size for the training set:

• Not specified.

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

• Not specified.

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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 < 40% for further referral or diagnostic follow-up. It is intended as the basis for further testing and is not intended to provide diagnostic quality output. As an integral part of a physical assessment, clinician's interpretations of this data can help identify previously undiagnosed left ventricular dysfunction in a patient.

The ELEFT consists of the following algorithm components:
· Eko Low Ejection Fraction Tool API
· Waveform Analysis:

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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