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The RNK Electronic Stethoscope is intended for use in detecting and amplifying heart, lung and other body sounds for diagnostic or monitoring purposes on any patient undergoing a physical assessment.

The RNK Electronic Stethoscope is comprised of a Chest Piece assembly, an Amplifier module containing an amplifier, and a standard audio Headset. The Chest Piece assembly and Headset are detachable and can plug into the Amplifier module. The Amplifier module amplifies and filters the signal from the Chest Piece and presents it to the attached Headset. A standard 3.5 mm audio cable is used to connect the RNK Chest Piece Assembly to the RNK Amplifier. A standard, off-the-shelf Headset with a 3.5 mm plug can plug into the RNK Amplifier to enable a listener to hear the sounds from the RNK Chest Piece. The RNK Amplifier is powered by x2 AAA batteries, which can be easily accessed for replacement. Unplugging the RNK Chest Piece puts the Amplifier into low power mode. The RNK Electronic Stethoscope provides up to about 20 times greater amplitude signal than a typical acoustic stethoscope. The overall auscultation frequency response of the RNK Electronic Stethoscope is 20 Hz - 1,500 Hz.

The provided text is a 510(k) summary for the RNK Electronic Stethoscope. It focuses on demonstrating substantial equivalence to predicate devices rather than proving the device meets specific acceptance criteria through a standalone study with performance metrics. Therefore, many of the requested details about acceptance criteria, detailed study design, and performance metrics are not explicitly available in this document.

However, I can extract information related to the device description, intended use, and the nature of the evaluation for substantial equivalence.

Here's a breakdown of the requested information based on the provided text:

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

This document does not explicitly state specific quantitative acceptance criteria or reported device performance metrics in a table format. The primary demonstration of effectiveness is through user testing to show substantial equivalence.

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 mentions "user testing with clinicians" but does not specify the sample size of this test set, the data provenance (country of origin), or whether it was retrospective or prospective.

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. The user testing is described generally as being with "clinicians," without specifying their number or qualifications.

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

The document does not describe any specific adjudication method for the user testing.

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

There is no mention of an MRMC comparative effectiveness study, nor is there any AI component described. The device is an electronic stethoscope intended to amplify and filter body sounds for human clinicians.

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

This is not applicable as the device is an electronic stethoscope, designed to be used by a human listener. It's not an algorithm-only device.

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

The document implies that the ground truth for "effectiveness" in the user testing was based on clinicians' assessment during the user testing, likely comparing the device's performance to predicate devices in identifying and hearing body sounds. No explicit "ground truth" method (like pathology or outcomes data) is described.

8. The sample size for the training set

This is not applicable. The device is a hardware electronic stethoscope and not an AI/ML algorithm that requires a training set.

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

This is not applicable. The device is a hardware electronic stethoscope and not an AI/ML algorithm that requires a training set and established ground truth for it.

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