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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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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:

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Stethophone v1 is intended for medical diagnostic purposes only. It may be used for detection and amplification of sounds from the heart and lungs with the use of selective frequency ranges. It can be used on adults undergoing physical assessment.

Stethophone v1 is an electronic stethoscope software application that operates on smartphones. Stethophone v1 allows for the capture and amplification of chest sounds that are listened in real-time or recorded, cloud storage of sound records, sound filtering for selected frequency ranges, and visualization to assist in sound analysis.

Stethophone v1 is designed to assist healthcare providers to hear and visualize heart and lung sounds during a physical examination of a patient as well as storing recorded sounds for later analysis. Stethophone v1 is a decision support device used for the assessment of chest sounds of adult patients in clinical and non-clinical environments. Key product features:

• Acquiring sound through the smartphone microphone
• Real-time listening of chest sounds
• Recording of chest sounds
• Two modes of sound visualization: oscillogram and spectrogram
• Selecting among three sound filters for listening:
  • Bell: Classic filter used for low frequency sounds
  • Diaphraqm: Classic filter used for higher frequency sounds of heart and lungs
  • Starling: Filter for listening to the full frequency of chest sounds

Here's a breakdown of the acceptance criteria and the study proving the Stethophone v1 device meets them, based on the provided FDA 510(k) summary. I will extract the relevant information and present it in the requested format.

It's important to note that this document is a 510(k) summary for a substantial equivalence determination, not a full clinical study report. Therefore, some details typically found in a comprehensive clinical trial description (like specific statistical methods for power calculation, detailed blinding procedures, or a full MRMC breakdown) might not be present or as detailed as one would expect from a dedicated clinical study publication.

Acceptance Criteria and Device Performance

The document states that Sparrow Acoustics Inc. submitted performance testing information "to validate that the device meets predetermined specifications and performs according to pre-specified acceptance criteria." However, it does not explicitly list a table of acceptance criteria with corresponding performance metrics. Instead, it describes various tests conducted to support safety and efficacy.

Based on the nature of the device (an electronic stethoscope software) and the information provided, the acceptance criteria would revolve around:

• Acoustic Performance: Ability to accurately capture, amplify, and filter heart and lung sounds within specified frequency ranges.
• Functional Performance: Real-time listening, recording, playback, sound visualization (oscillogram, spectrogram), filter selection, volume control, and smartphone compatibility.
• Usability: Ease of use for healthcare providers.
• Safety: No electrical safety or electromagnetic compatibility issues (due to being standalone software).

Given the absence of a specific table, the "reported device performance" is inferred from the claims of "substantially equivalent" and the successful completion of the described tests.

Here is a conceptual table based on the provided text, outlining the implied acceptance criteria and the claimed performance.

Table 1: Implied Acceptance Criteria and Reported Device Performance

Study Details

Here's a breakdown of the study details as presented in the 510(k) summary:

1. Sample sizes used for the test set and the data provenance:

   • The document mentions "performance tests using an anechoic chamber," "internal tests run by a medical analysts' team," "tests involving external medical specialists with auscultation experience," and "a usability and performance study conducted by an independent medical facility."
   • However, specific sample sizes (e.g., number of audio recordings, number of subjects for usability, etc.) for any of these test sets are NOT explicitly stated in this summary.
   • Data Provenance: Not explicitly stated, but the company is Canadian (Sparrow Acoustics Inc., Lucasville, NS, Canada). The nature of the tests (internal, external specialists, independent facility) suggests some form of prospective testing for performance and usability. It does not mention retrospective data analysis.

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

   • The document refers to "a medical analysts' team" and "external medical specialists with auscultation experience."
   • The exact number of experts and their specific qualifications (e.g., "radiologist with 10 years of experience" or "cardiologist") are NOT specified. It only indicates "auscultation experience," which is a broad term.

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

   • The document does NOT describe any specific adjudication method (like "2+1" or "3+1 consensus") for establishing ground truth from the medical analysts or specialists. It simply states that they were involved in internal and external testing.

4. 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:

   • A formal MRMC comparative effectiveness study in the sense of comparing human readers with AI assistance versus without AI assistance was NOT described or implied.
   • The testing focused on the device's standalone performance, its acoustic properties, and its usability, aiming to demonstrate substantial equivalence to predicate devices, not improvement of human readers. The device is described as a "decision support device" but the efficacy study is not structured as an MRMC human-in-the-loop study.

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

   • Yes, performance of the device's sound capturing, amplification, and filtering capabilities were evaluated, including "performance tests using an anechoic chamber." This can be considered a form of standalone testing for the device's acoustic properties.

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

   • For the acoustic performance testing (e.g., anechoic chamber), the "ground truth" would be established by controlled sound inputs and calibrated measurement equipment.
   • For the tests involving "medical analysts' team" and "external medical specialists," the ground truth likely involved the clinical judgment or consensus of these experts regarding the quality, clarity, and diagnostic utility of the sounds captured and presented by the Stethophone v1 compared to their auscultation experience with predicate devices. No pathology or outcomes data is mentioned.

7. The sample size for the training set:

   • The Stethophone v1 is described as an "electronic stethoscope software application" that provides "sound filtering for selected frequency ranges, and visualization to assist in sound analysis." It is not described as an AI/ML algorithm that requires a training set in the traditional sense (i.e., for learning patterns for classification or prediction).
   • It functions as an audio processing and visualization tool. Therefore, a training set in the context of machine learning is not applicable or mentioned for this device. Its "training" would be more akin to software development and acoustic engineering calibration.

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

   • As there is no mention of a traditional machine learning "training set" for the Stethophone v1, this question is not applicable based on the provided document. The device's functionality relies on signal processing and displaying capabilities, not on learned patterns from a labelled dataset.

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