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Stethophone Pro is an electronic stethoscope that enables detection, amplification, filtering, and transmission of auscultation sound data (heart and lungs), whereby a clinician at one location can listen to the auscultation sounds of a patient acquired on site or at a different location. Stethophone Pro is intended for use on adult patients. Stethophone Pro is not intended for self-diagnosis and not intended to be used as a sole means of diagnosis. Stethophone Pro can be used in clinical and nonclinical environments.

For Rx-only: Stethophone Pro is intended to provide decision support to clinicians in their evaluation of patients' heart sounds. The software analyzes heart sounds and phonocardiograms and can automatically detect murmurs that may be present, sound timing and character, including S1, S2, and the absence of a heart murmur. The interpretations of heart sounds offered by the software are not diagnoses and are meant only to provide decision support to the clinician, who may use the result in conjunction with their own evaluation and clinical judgment.

For OTC: When used without access to the automatic analysis feature or under supervision of healthcare professional, Stethophone Pro is also intended to be used by lay users.

Stethophone Pro is an electronic stethoscope software application that operates on smartphones. Stethophone enables the capture and amplification of chest sounds for real-time or recorded listening. Cloud storage with sound record sending capabilities, filtering for selected frequency ranges, and visualization all assist with sound analysis.

Stethophone is designed to assist healthcare professionals in both hearing and visualizing heart and lung sounds during a physical examination of a patient and in storing recorded sounds in the cloud for later analysis. It also enables home users to record and send chest sounds to their physicians.

Stethophone can be used for the assessment of chest sounds of adult patients in clinical and non-clinical environments. Assessment should be performed by healthcare professionals, while sound capturing can be performed by both healthcare professionals and home users.

While all functions described above are available to both prescription and OTC users, the following analysis features and the associated report are accessible exclusively with a prescription: Stethophone Pro performs basic analysis of heart sounds, including detection of murmurs, identification of S1 and S2 heartbeats on the timeline, and calculation of timing intervals between them.

Key Product Features Available for both prescription and OTC users

• Capturing chest sounds using the smartphone microphone:
  • Real-time listening to chest sounds
  • Recording of chest sounds
• Sending examinations to specialists for assessment
• Two modes of sound visualization: oscillogram and spectrogram
• Selecting from three audio filters for listening:
  • Bell Traditional filter used in stethoscopes for low frequency sounds
  • Diaphragm Traditional filter used for higher frequency sounds of heart and lungs
  • Starling Filter for listening to the full frequency of chest sounds

All of these features are present in both the primary predicate, Stethophone Pro (K240901), and the reference device, Stethophone (K231551), and are now available for OTC use.

Product Features For prescription users only

• Detecting murmurs, timing for S1 and S2 sounds, and calculating heart rate

These features are derived from the Stethophone Pro (K240901, Rx-only).

Collectively, these features enable home users to acquire their own sounds, share them with healthcare professionals, and control their health, as well as allow healthcare professionals to examine and monitor patients on site and remotely, seek out second opinions from specialists, and use the device in a telemedicine context.

Based on the provided FDA 510(k) clearance letter for Stethophone Pro (K252595), here's an analysis of the acceptance criteria and study information:

The document explicitly states that no new performance data was required for this submission. This is because the device is a modified version of previously cleared devices (Stethophone Pro K240901 and Stethophone K231551), and the modifications were solely to the indications for use, not to the underlying acoustics algorithms or automated sound analysis capabilities.

Therefore, the following information is not available in the provided document, as no new studies were conducted for this specific clearance (K252595):

• A table of acceptance criteria and the reported device performance
• Sample sizes used for the test set
• Data provenance for the test set
• Number of experts used to establish ground truth for the test set
• Qualifications of experts
• Adjudication method for the test set
• Whether a multi-reader multi-case (MRMC) comparative effectiveness study was done
• Effect size of human reader improvement with AI vs. without AI assistance
• If a standalone performance (algorithm only without human-in-the-loop performance) was done
• Type of ground truth used (expert consensus, pathology, outcomes data, etc.)
• Sample size for the training set
• How the ground truth for the training set was established

However, we can infer that acceptance criteria and performance data were established and met in the previous clearances (K240901 and K231551) for the functionalities that remain unchanged.

Summary of Device Performance and Acceptance Criteria (Based on Previous Clearances - Not explicitly detailed in this document):

The document states:

• "The acoustics algorithms for sound filtering (that are used for both OTC and Rx parts of the software) capabilities remain entirely unchanged from those validated and cleared in both K240901 and K231551."
• "Automated sound analysis capabilities (that are available for Rx use only) remain entirely unchanged from those validated and cleared in K240901."

This implies that for the features that do have performance claims (specifically, automated sound analysis for murmur detection and heart sound timing in the Rx-only version), acceptance criteria were met during the clearance of K240901. Similarly, for the basic functions of sound capture, amplification, filtering, and transmission, acceptance criteria were met during the clearance of K231551 and K240901.

To obtain the specific details regarding the acceptance criteria, study designs, sample sizes, and ground truth methodologies for these functionalities, one would need to refer to the original 510(k) submissions for K240901 and K231551. This current document (K252595) serves as a modification that leverages the previously established substantial equivalence and performance data.

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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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The AS-101 is an electronic stethoscope intended for the detection and amplification of sounds associated with the heart, lungs, arteries, veins, and other internal organs. It can be used on any person undergoing a physical assessment. The device is intended to be operated only by healthcare professionals for diagnostic decision support in clinical settings.

This submission is for device "The AccurSound Electronic Stethoscope AS-101 ("AS-101")". This submission expands upon a previously 510(k)-cleared device(K221805) by introducing two new reusable sensors, whereas the original device only included disposable sensors..

The AccurSound Electronic Stethoscope AS-101 ("AS-101") in this submission is a device designed for healthcare professionals used in clinical settings. The AS-101 can detect and amplify the sounds of the heart, lungs, arteries, veins, and other internal organs.

The microphone-equipped sensor detects and amplifies the sounds from the patient's body. The auscultation sound is digitally processed and filtered, electronically amplified in the hub unit. The anti-noise function reduces ambient noise and echoes, then transferred to the earpiece.

The multi-channel design allows healthcare professionals to attach disposable sensors or reusable sensors onto patient's body, by switching modes from handheld single-channel recording to four-channel stationery and continuously auscultation based on different requirements of clinical applications or physical assessments.

The provided text is a 510(k) Premarket Notification for the AccurSound Electronic Stethoscope (AS101), which is a modification of a previously cleared device (K221805). The modification involves the introduction of two new reusable sensors, whereas the original device only included disposable sensors. Based on the document, here's an analysis of the acceptance criteria and supporting studies:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not explicitly present a table of acceptance criteria with corresponding performance results for specific clinical metrics. Instead, it relies on non-clinical testing and the substantial equivalence to the predicate device. The performance tests mentioned are general and not detailed with specific quantitative acceptance criteria in this document.

However, based on the information provided, the following non-clinical tests were conducted to ensure safety and performance:

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

The document explicitly states: "This submission does NOT include animal or clinical performance testing." Therefore, there is no clinical test set, sample size, or data provenance (country of origin, retrospective/prospective) to report for the primary evaluation of this device (the modified AS-101 with reusable sensors). The assessment relies on non-clinical bench testing and the substantial equivalence to the predicate device.

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

As there was no clinical study conducted for this submission (specifically for the modified device), there is no information on experts used to establish ground truth.

4. Adjudication Method for the Test Set

Not applicable as there was no clinical test set.

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

No MRMC comparative effectiveness study was done. The document states: "This submission does NOT include animal or clinical performance testing."

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

Not applicable. This device is an electronic stethoscope intended for use by healthcare professionals for direct patient assessment, not an AI algorithm acting in a standalone capacity. The "diagnosis decision support" refers to the device aiding the human professional, not replacing them or offering an automated diagnosis.

7. Type of Ground Truth Used

For the non-clinical tests conducted, the "ground truth" would be established by the industry standards and regulatory requirements themselves (e.g., passing specific electrical safety thresholds, demonstrating biocompatibility as per ISO standards, software functioning as specified). There is no "ground truth" in the sense of clinical reference diagnoses (e.g., pathology, outcomes data, expert consensus) because no clinical performance testing was performed for this submission.

8. Sample Size for the Training Set

Not applicable. The AccurSound Electronic Stethoscope is a hardware device for sound amplification and filtering. It does not appear to incorporate machine learning or AI that would require a "training set" in the conventional sense of AI/ML algorithms. The software mentioned is for device control and processing, verified through standard software validation, not through learning from data.

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

Not applicable, as there is no training set mentioned or implied.

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Stethophone Pro is an electronic stethoscope that enables detection, filtering, and transmission of auscultation sound data (heart and lungs).

Stethophone Pro is intended to provide decision support to clinicians in their evaluation of patients heart sounds. The software analyzes heart sounds and phonocardiograms and can automatically detect murmurs that may be present, sound timing and character, including S1, S2, and the absence of a heart murmur.

Stethophone Pro is not intended to be used as a sole means of diagnosis and is for use in environments where health care is provided by clinicians. The interpretations offered by the software are meant only to provide decision support to the clinician, who may use the result in conjunction with their own evaluation and clinical judgment. The interpretations are not diagnoses. Stethophone Pro is intended for use on adult patients.

Stethophone Pro is an electronic stethoscope software application that operates on smartphones. Stethophone Pro is designed for use by healthcare professionals or on the order of healthcare professionals.
Stethophone Pro enables the capture and amplification of chest sounds for real-time or recorded listening. Cloud storage with sound record sending capabilities, filtering for selected frequency ranges, and visualization all assist with sound analysis.

Stethophone Pro is designed to assist healthcare professionals in both hearing and visualizing heart and lung sounds during a physical examination of a patient and in storing recorded sounds in the cloud for later analysis. It also enables home users to record and send chest sounds to their physicians.

Stethophone Pro can be used for the assessment of chest sounds of adult patients in clinical and non-clinical environments. Assessment is performed by healthcare professionals, while sound capturing can be performed by both healthcare professionals and home users.

Stethophone Pro performs basic analysis of heart sounds allowing to detect the presence of murmurs, locate heartbeats on the timeline (S1/S2), and calculate timing between them.

Key product features:

• Capturing chest sounds using the smartphone microphone:
  • Real-time listening to chest sounds,
  • Recording of chest sounds,
• Sending examinations to specialists for assessment,
• Two modes of sound visualization: oscillogram and spectrogram,
• Detecting murmurs, timing for S1 and S2 sounds, and calculating heart rate,
• Selecting from three audio filters for listening:
  • Bell: Traditional filter used in stethoscopes for low frequency sounds,
  • Diaphragm: Traditional filter used for higher frequency sounds of heart and lungs, and
  • Starling: Filter for listening to the full frequency of chest sounds.

Collectively, these features enable users to acquire heart sounds and receive basic reporting, so as enable healthcare professionals to examine and monitor patients on site and remotely, seek out second opinions from specialists, and use the device in a telemedicine context.

Here's a breakdown of the acceptance criteria and study details for the Stethophone Pro, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state acceptance criteria in a pass/fail form (e.g., "must achieve >X% sensitivity"). Instead, it presents the reported performance metrics. For this response, I will list the reported performance metrics as the de facto "met performance" from the validation study.

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

• Sample Size (Test Set): 7,304 heart sound recordings from 2,277 adult subjects.
• Data Provenance: The data consisted of both proprietary and public clinical data. 4,396 recordings were from a proprietary dataset, recorded using various devices including smartphones and commercially available stethoscopes. Data appears to be from adult subjects ranging from 18 to 91 years old. Ethnicity representation: 86.1% white, 6.1% Latino, 5.0% Asian, and 2.7% African American. The country of origin for the "America" dataset or "Multi-Device" dataset is not explicitly stated but implies a broad representation. The study was retrospective.

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

• Number of Experts: "Multiple expert cardiologists." The exact number is not specified beyond "multiple".
• Qualifications: "Expert cardiologists." Specific experience levels (e.g., "10 years of experience") are not provided.

4. Adjudication Method for the Test Set

The document states that "Each recording in a testing dataset was annotated by multiple expert cardiologists." It does not explicitly describe an adjudication method (such as 2+1 or 3+1 consensus). It simply states they were annotated by multiple experts, implying that their annotations formed the ground truth, likely through consensus or independent review that established the final label.

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 was not explicitly stated or described. The performance data presented is for the standalone algorithm.

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

• Yes, a standalone performance study was done. The results presented in the tables (S1 precision, S2 sensitivity, Murmur detection, etc.) are for the "Stethophone Pro algorithms for heart sound analysis," without mentioning human-in-the-loop performance. The preamble to the performance data states: "Stethophone Pro underwent a thorough testing process to ensure its safety, reliability and effectiveness. Testing included both software verification and validation, as well as clinical validation." And "Stethophone Pro algorithms for heart sound analysis have been validated in both retrospective and clinical performance testing..." This confirms standalone algorithm performance.

7. The Type of Ground Truth Used

• Expert Consensus: The ground truth for heart sound analysis (presence of murmur, S1/S2 timings) was established by "multiple expert cardiologists" who annotated each recording.

8. The Sample Size for the Training Set

• The sample size for the training set is not provided in the document. It only states that there was "no overlap between subjects and recordings included in the testing and training data."

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. It only mentions that the "validation was performed after the algorithm development and training was finalized."

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The Keikku is an electronic stethoscope that enables amplification, filtering, and transmission of auscultation data of the patient (heart, lungs, bowel, arteries, and veins), whereby a clinician at one location on a network can listen to the auscultation data of a patient on site or at a different location on the network. The Keikku is intended for use on pediatric and adult patients. The Keikku is intended to be used by professional users in a clinical environment or by lay users in a nonclinical environment. The device is not intended for self-diagnosis.

The Keikku (Rx) is a digital stethoscope device designed for use by health care professionals in clinical settings and by lay users in non-clinical environments under healthcare provider supervision. The Keikku electronically amplifies, filters and transfers body sounds through the accompanying mobile application and is used for storage, sharing and transmitting the data for telemedicine use. It also enables lay users, under supervision from a healthcare provider, to listen to their body sounds (lungs, heart, arteries, veins, gastrointestinal tract, etc.), record and share it with their physicians during telehealth sessions. The Keikku consists of two primary components: 1. The Keikku device is an electronic stethoscope. The Keikku device is used for recording audio, converting it to digital data, and transmitting the data to a mobile device via Bluetooth®. It includes volume adjustment via rotation, tap feature for starting and ending the recording, and an LED light indicator for indicating the status of the device. 2. The Keikku App. The app captures audio data from the Keikku device and provides data visualization and annotation, secure data storage, audio playback, and sharing features. These features enable a healthcare professional to monitor patients, seek second opinions from a specialist or use the device for telemedicine use.

The provided text describes the Keikku Electronic Stethoscope and its substantial equivalence determination to predicate devices. However, it does not contain specific acceptance criteria or a detailed study that proves the device meets such criteria in the format explicitly requested.

The document states that "The test passed and met the predefined acceptance criteria" for performance testing related to audio frequency and NSR response, but it does not specify what those acceptance criteria were or present the reported device performance in a table. It also refers to usability evaluation as having "passing results" without detailing the study or its criteria.

Therefore, I cannot fully complete the requested table and answer all questions due to the lack of explicit information in the provided text.

Based on the available information, here is what can be extracted and what is missing:

1. A table of acceptance criteria and the reported device performance

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

• Sample Size for Test Set: Not specified in the provided text for any of the performance studies.
• Data Provenance: Not specified in the provided text.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

• Not specified. The document repeatedly refers to "tests" and "evaluations" but does not mention expert involvement in establishing ground truth for any test sets beyond general usability studies.

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

• Not specified.

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 is mentioned. The device's primary function is as an electronic stethoscope for amplification, filtering, and transmission of auscultation data, not an AI-assisted diagnostic tool for human readers.

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

• This question is not directly applicable in the context of an electronic stethoscope as described. The device itself (Keikku Electronic Stethoscope) performs sound capture, amplification, and filtering. It is inherently a "standalone" device in its primary functionality. However, it works with an accompanying mobile application for data visualization, storage, and sharing, and facilitates human practitioners' listening. There's no separate "algorithm only" performance reported that would be distinct from the device's inherent operation.

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

• The document primarily describes engineering and validation testing (biocompatibility, cleaning/disinfection, electrical safety, EMC, software). For the "Performance Testing," it states the purpose was "to verify the Keikku's performance is similar to that of its predicate and reference devices, Eko Core and 3M Littmann electronic stethoscope, in terms of audio frequency and NSR response." This implies the "ground truth" for performance was defined by the established performance characteristics of the predicate and reference devices, rather than a clinical ground truth like pathology or expert consensus on clinical diagnoses.

8. The sample size for the training set

• Not applicable as the document does not describe any machine learning models that would require a "training set." The Keikku device provides amplification, filtering, and transmission, but it's not described as having an AI component that is "trained" in the typical sense.

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

• Not applicable (see point 8).

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The CORE 500 Digital Stethoscope is intended to be used by clinicians or lay users 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 or by lay users).

A lay user is not intended to interpret or take clinical action based on the device output without consulting with a qualified healthcare professional.

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 clinicians or lay users. 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. The device can be used in a professional healthcare facility and for home use.

CORE 500 features three auscultation modes for a 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.

This FDA 510(k) summary for the Eko Health, Inc. CORE 500 Digital Stethoscope (K233609) describes the device's technical specifications and how it compares to a predicate device. Regarding acceptance criteria and detailed study results, the document provides a general overview rather than specific performance metrics.

Here's an analysis of the provided information concerning acceptance criteria and study details:

1. A table of acceptance criteria and the reported device performance

The document does not provide a table of acceptance criteria with corresponding reported device performance values for the CORE 500 Digital Stethoscope in the way one might expect for a clinical performance study. Instead, it lists the types of nonclinical testing performed and asserts that the device complies with standards or demonstrates performance.

Here's a summary of the reported performance without specific numerical acceptance criteria from the document:

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

The document does not provide specific sample sizes for test sets, data provenance, or whether studies were retrospective or prospective. The performance data section focuses on nonclinical testing.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

This information is not provided in the document. The performance data is described as "nonclinical testing" and does not appear to involve expert-adjudicated ground truth as typically found in clinical studies assessing diagnostic accuracy.

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

This information is not provided. As the document focuses on nonclinical performance, an adjudication method on a clinical test set is not described.

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

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study. The device, the CORE 500 Digital Stethoscope, is primarily an electronic stethoscope for amplifying, filtering, and transferring body sounds and ECG waveforms, and displaying ECG and heart rate. It is not described as having an AI diagnostic interpretation component that would typically be evaluated in an MRMC study with human readers.

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

The document does not explicitly state that a standalone (algorithm only) performance study was done for any specific AI functionality. The device displays ECG waveforms and heart rate, but the document does not describe it as having an autonomous diagnostic algorithm for complex conditions.

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

Given that the performance data described is "nonclinical testing" (bench testing, biocompatibility, electrical safety, software V&V, usability), the concept of "ground truth" as it applies to clinical diagnostic accuracy (e.g., expert consensus, pathology) is not applicable or described in this section. The testing would have focused on meeting technical specifications and regulatory standards.

8. The sample size for the training set

The document does not mention a training set or its sample size. This type of information would typically be provided for devices involving machine learning or AI algorithms with extensive training phases, which is not the primary focus of the performance data in this submission.

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

Since no training set is mentioned (see point 8), there is no information on how ground truth for a training set was established.

Summary of Device and Performance Context:

The K233609 submission for the CORE 500 Digital Stethoscope primarily focuses on demonstrating substantial equivalence to its predicate device (K230111) and a reference device (K200776), particularly for its expanded "Over-The-Counter Use" and inclusion of "lay users." The performance data provided are centered on foundational nonclinical tests to ensure safety, efficacy, and compliance with general device regulations and standards. It's not a submission for a novel diagnostic AI algorithm requiring extensive clinical performance studies with ground truth establishment by experts. The "nonclinical testing" confirms the device's technical functionality, biocompatibility, electrical safety, software validation, and usability for its intended purpose of amplifying, filtering, and transferring body sounds and ECG waveforms, and displaying basic heart rate and ECG.

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Stethophone is an electronic stethoscope that enables detection, amplification, filtering, and transmission of auscultation sound data (heart and lungs), whereby a clinician at one location can listen to the auscultation sounds of a patient acquired on site or at a different location. Stethophone is intended for use on adult patients. Stethophone is intended to be used by professional or lay users in a clinical or nonclinical environment. Stethophone is not intended for self-diagnosis.

Stethophone is an electronic stethoscope software application that operates on smartphones. Stethophone is designed for use by both healthcare professionals and home users.

Stethophone enables the capture and amplification of chest sounds for real-time or recorded listening. Cloud storage with sound record sending capabilities, filtering for selected frequency ranges, and visualization all assist with sound analysis.

Stethophone is designed to assist healthcare providers both in hearing and visualizing heart and lung sounds during a physical examination of a patient and in storing recorded sounds in cloud for later analysis. It also enables home users to record and send chest sounds to their physicians.

Stethophone is a decision support device used for the assessment of chest sounds of adult patients in clinical and non-clinical environments. Assessment is performed by healthcare providers, while sound capturing can be performed by both healthcare providers and home users.

Key product features:

• Capturing chest sounds using the smartphone microphone:
  • Real-time listening to chest sounds,
  • Recording of chest sounds,
• Sending examinations to specialists for assessment
• Two modes of sound visualization: oscillogram and spectrogram, and
• Selection of three audio filters for listening:
  • Bell: Traditional filter used for low frequency sounds,
  • Diaphragm: Traditional filter used for higher frequency sounds of heart and lungs, and
  • Starling: Filter for listening to the full frequency of chest sounds.

Collectively, these features enable a healthcare professional to examine and monitor patients, seek out second opinions from specialists, and use the device in a telemedicine context.

The provided text does not contain detailed information about the acceptance criteria or a specific study that proves the device meets those criteria in the typical format expected for comprehensive regulatory submissions. The document is an FDA 510(k) summary, which provides a high-level overview.

However, based on the available text, here's what can be extracted and inferred:

1. Table of Acceptance Criteria and Reported Device Performance

The document states: "Sparrow Acoustics Inc. submitted performance testing information in this 510(k) to demonstrate safety and efficacy of Stethophone, to validate that the device meets predetermined specifications and performs according to pre-specified acceptance criteria, and to support the substantial equivalence determination."

It also mentions the types of tests conducted: "Testing includes repeatability and reproducibility tests, performance tests using an anechoic chamber, internal tests run by a medical analysts' team, tests involving lay users and external medical specialists with auscultation experience."

Without the full performance testing report, specific numeric acceptance criteria and detailed reported performance cannot be provided in a table format. The summary only broadly states that the device "meets predetermined specifications and performs according to pre-specified acceptance criteria."

Here's an example of what such a table would look like if the specific data were available:

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

The document mentions "tests involving lay users and external medical specialists with auscultation experience." However, it does not specify the sample size for the test set nor the data provenance (e.g., country of origin, retrospective or prospective).

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

The document indicates "external medical specialists with auscultation experience" were involved. It does not specify the 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 mention any specific adjudication method (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

The document is for an electronic stethoscope software, not an AI-assisted diagnostic tool in the sense of image interpretation. It describes features like "amplification, filtering, and transmission of auscultation sound data (heart and lungs)" and "Cloud storage with sound record sending capabilities, filtering for selected frequency ranges, and visualization." While it offers "decision support," it doesn't describe an AI algorithm providing diagnostic interpretations or classifications that would lend itself to a traditional MRMC study comparing human reader performance with and without AI assistance for interpretation. Therefore, a multi-reader multi-case (MRMC) comparative effectiveness study of human readers improving with AI vs. without AI assistance was not conducted or reported in this summary.

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

The device is an "electronic stethoscope software application that operates on smartphones" and is intended for use by "healthcare providers" and "home users" for capturing and aiding in listening/visualizing sounds. It is explicitly stated as a "decision support device" and "not intended for self-diagnosis." This phrasing suggests a human-in-the-loop context where the device assists a user, rather than functioning as a standalone diagnostic algorithm. Therefore, a standalone algorithm-only performance study in the sense of a fully automated diagnostic output does not seem applicable or described here. The performance tests mentioned focus on the technical aspects of sound capture, amplification, filtering, repeatability, and usability by human users.

7. The type of ground truth used

For tests involving "lay users and external medical specialists with auscultation experience," the ground truth for assessing performance (e.g., sound quality, clarity, ability to discern features) would likely be based on expert consensus or expert evaluation of the captured sounds against a known "true" sound or a reference standard. However, the document does not explicitly state the type of ground truth used.

8. The sample size for the training set

The document does not mention a training set as it is focusing on the performance of the Stethophone device, which is an electronic stethoscope software, not a machine learning model that would typically require a training set in the context of diagnostic AI.

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

As there's no mention of a traditional training set for a machine learning model, this information is not applicable based on the provided document. The device's functionality primarily revolves around audio processing (amplification, filtering) and transmission, rather than de novo diagnostic inference from complex data patterns that would necessitate a large, labeled training dataset.

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SKEEPER is an electronic stethoscope that collects, fitters, and amplifies the sound of a person's heart, lungs, and abdomen. It is intended for use by professional users in clinical environments or by lay users in non-clinical environments. It is not intended for self-diagnosis.

SKEEPER is an electronic stethoscope used to collect and measure the sounds inside the body, and it consists of an electronic stethoscope (SM-300) and a mobile application (SKEEPER PRO APP).

When the diaphragm at the bottom of the SM-300 is lightly attached to the body part to be measured, such as the heart, lungs, or abdomen, the diaphragm vibrates due to the sound inside the human body and generates a sound. This sound is collected using a microphone and converted into an electrical signal. After that, the electrical signal is filtered and amplified for each frequency band set according to the measurement site, amplified, and output to earphones or transmitted to the SKEEPER APP installed on the mobile platform, and then the sound is output the earphone or speaker of the corresponding platform.

The SKEEPER PRO APP graphs the waveform of the sound received by the SM-300. It also stores and plays back sound data and analyzes heart rate.

The provided text is a 510(k) summary for the SKEEPER electronic stethoscope. It describes the device, its intended use, and a comparison to predicate and reference devices to demonstrate substantial equivalence. However, it does not contain the detailed acceptance criteria, device performance, or information about specific studies (like a multi-reader multi-case study, standalone performance, sample sizes for test/training sets, expert qualifications, adjudication methods, or ground truth establishment) as requested in your prompt.

Specifically, the document states:

• "Clinical Data was not required to demonstrate the substantial equivalence." This indicates that no clinical study was performed.
• Non-clinical data provided includes electrical safety, electromagnetic compatibility, biocompatibility, software verification and validation, wireless coexistence, and bench testing. These typically involve engineering and performance specifications but not diagnostic accuracy or comparative effectiveness in a clinical context.

Therefore, I cannot provide the requested information from the given text as it does not contain details about:

1. A table of acceptance criteria and reported device performance related to diagnostic accuracy or clinical utility.
2. Sample sizes for test sets, data provenance, number of experts, adjudication methods.
3. Multi-reader multi-case comparative effectiveness study or standalone performance.
4. Types of ground truth used for such studies.
5. Sample size for the training set or how ground truth was established for it.

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The AusculThing ACC software is a decision-support SW for the healthcare provider (the user) in the evaluation of patient heart sounds. The ACC is used to record, display, and analyze acoustic signals of the heart recorded by means of an electronic stethoscope. It is intended for use on adult and pediatric patients. The automated analysis will categorize heart sounds as either "abnormal" if any heart murmur of any intensity is identified in any position across the precordium, or "normal" if either no murmurs or benign murmurs are identified. ACC is indicated for use in a setting where auscultation would typically be performed by a healthcare provider. It is not intended as a sole means of diagnosis. The heart sound interpretation offered by the software is only significant when used in conjunction with physician over-read and including all other relevant patient data. The device is intended for Rx use only. The AusculThing ACC shall be used together with Thinklabs One electronic stethoscope.

AusculThing ACC is a decision support SW that collects heart sounds from adult and pediatric patients. The ACC software receives the data using a Thinklabs One electronic stethoscope. The SW is running on a mobile device, where the electronic stethoscope is connected to. The SW guides the user how relevant heart sound recordings should be obtained from different parts of the body. After recording, the ACC analyzes the recordings in conjunction automatically using an AI -based algorithm, which is trained using a proprietary echocardiogram validated high-quality data database. The basic functionality of the ACC SW is to give a user an instant, automated, analysis of the patient under evaluation and differentiate between normal and pathological sounds. For the abnormal heart sounds, the ACC delivers information on suspected murmurs. The ACC software is a SW that allows a user to upload heart sounds/phonocardiogram (PCG) data to the device for analysis and visualization. The AusculThing ACC Mobile App runs on a mobile device. The app permits the electronic recording of heart sound signals via a compatible electronic stethoscope (Thinklabs One). The app also permits visual and acoustic playback of heart sounds in the mobile device. After analysis, results are returned to the user in the App. The Murmur detection algorithm is based on a neural network model that uses heart sounds to detect the presence of pathological heart sounds. The user can utilize the heart sound analysis results and the acoustic and visual representation of the heart sound recordings as decision support data in their decision-making process regarding the presence and type of a heart murmur.

The AusculThing ACC device claims substantial equivalence to the predicate device, eMurmur ID (K181988), for its performance in detecting abnormal heart sounds.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the AusculThing ACC are based on demonstrating non-inferiority to the predicate device, eMurmur ID, in terms of sensitivity, specificity, and accuracy.

The reported performance of the AusculThing ACC (Sensitivity 90.5%, Specificity 96.0%, Accuracy 92.5%) exceeds the performance metrics of the predicate device, eMurmur ID, thereby demonstrating non-inferiority.

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

• Sample Size: The test set comprised 133 patients, from whom a total of 519 heart sound recordings were captured.
  • 84 patients were below 18 years of age.
  • 49 patients were above 18 years of age.
  • 84 patients had a confirmed heart defect.
• Data Provenance: All data was collected in a clinical study conducted in Finland across various hospitals:
  • Children:
    • Kuopio University Hospital (Puijo Hospital)
    • Oulu University Hospital
  • Adults:
    • Hospital district of Helsinki and Uusimaa (Lohja Hospital)
    • Hospital district of Helsinki and Uusimaa (Hyvinkää Hospital)
      The study was conducted in accordance with GCP/ISO14155, indicating a prospective and ethically sound approach to data collection.

3. Number of Experts Used to Establish Ground Truth and Qualifications

• Number of Experts: Not explicitly stated as a number, but the ground truth was established by cardiologists.
• Qualifications: The heart sound recordings were obtained by a cardiologist, and an echocardiogram was conducted by a cardiologist on all patients to establish the golden standard for diagnosis. This implies highly qualified medical professionals experienced in cardiovascular diagnosis.

4. Adjudication Method for the Test Set

The document does not explicitly state an adjudication method (e.g., 2+1, 3+1). It states that an echocardiogram was conducted by a cardiologist on all patients to establish the "golden standard for diagnosis," suggesting that the cardiologist's echocardiogram interpretation served as the definitive ground truth for each case. This implies a single-expert gold standard based on the cardiologist's assessment and the echocardiogram.

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

• A MRMC comparative effectiveness study was not explicitly conducted or reported in this summary. The comparison is between the standalone performance of the AusculThing ACC algorithm and the reported performance of the predicate device's algorithm, not the improvement of human readers with AI assistance.

6. Standalone (Algorithm Only) Performance

• Yes, a standalone performance study was conducted. The reported sensitivity, specificity, and accuracy values (90.5%, 96.0%, 92.5%) are for the AusculThing ACC algorithm itself, without a human-in-the-loop component for the performance evaluation presented. The device is intended as "decision support SW" and "not intended as a sole means of diagnosis," with interpretation significant "in conjunction with physician over-read," but the reported performance metrics are for the algorithm's direct classification output.

7. Type of Ground Truth Used

• The ground truth used was expert consensus combined with pathology/diagnostic imaging. Specifically, a cardiologist performed an echocardiogram on all patients, which was then used to establish the "golden standard for diagnosis" against which the algorithm's performance was compared.

8. Sample Size for the Training Set

• The document states that the AI-based algorithm was "trained using a proprietary echocardiogram validated high-quality data database." However, the sample size for this training set is not provided in the given text.

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

• The ground truth for the training set was established using a "proprietary echocardiogram validated high-quality data database." This implies that the training data also had ground truth labels derived from echocardiogram interpretations, likely by cardiologists, similar to how the ground truth for the test set was established. However, specific details about the process for the training set are not provided beyond this general statement.

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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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