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510(k) Data Aggregation
(135 days)
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:
| Acceptance Criterion | Reported Device Performance |
|---|---|
| [Specific metric, e.g., Frequency Response Accuracy (Bell Filter)] | [Specific measured value vs. accepted range, e.g., "Within +/- 3dB of target response across 25-300 Hz"] |
| [Specific metric, e.g., Amplification Gain Consistency] | [Specific measured value vs. accepted range, e.g., "Standard deviation of amplification gain < 5% across devices"] |
| [Specific metric, e.g., Sound Clarity (Qualitative Rating)] | [Specific score/rating vs. accepted threshold, e.g., "Average expert rating > 4 on a 5-point scale for clarity"] |
| [Specific metric, e.g., Repeatability (Inter-measurement variability)] | [Specific statistical measure vs. accepted threshold, e.g., "Coefficient of Variation < 10%"] |
| [Specific metric, e.g., Usability for Lay Users] | [Specific success rate/feedback score vs. accepted threshold, e.g., "95% task completion rate for lay users"] |
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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