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The Peace N Quiet device is indicated for use in the temporary relief of tinnitus symptoms. The device plays customized sounds to relieve patients suffering from tinnitus and can be used in a tinnitus management program. The target population is for patients who are 18 years or older. This device should only be used with the advice of a physician, audiologist or other healthcare professional.

The Peace N Quiet tinnitus device is software as a medical device implemented as a mobile application for patients suffering from tinnitus. The Peace N Quiet tinnitus mobile app can be downloaded from the Apple Store onto a personal Apple iPhone device facilitates a qualified healthcare professional (HCP). i.e., physician or audiologist, to provide professional counseling or education to the patient. The HCPs can direct and assist patients to self-administer customized treatments using the Peace N Quiet tinnitus device.

The provided document is a 510(k) Premarket Notification from the FDA for a device called "Peace N Quiet (0.7.0)", which is a software as a medical device (SaMD) intended for the temporary relief of tinnitus symptoms.

Based on the document, here's an analysis of the acceptance criteria and the study that proves the device meets them:

Crucially, the document states: "SUMMARY OF CLINICAL TESTING: Clinical testing was not performed as part of this submission." This means there is no clinical study described in this document to demonstrate the device's performance against acceptance criteria for clinical efficacy or human user performance.

The document focuses on demonstrating substantial equivalence to predicate devices through a comparison of technological characteristics and non-clinical testing (software verification and validation). It does not present acceptance criteria or a study that proves the device meets clinical performance criteria in the way one might expect for a diagnostic or therapeutic AI/ML device that generates specific outputs for classification or prediction.

Therefore, for many of the requested points, the answer will be "Not applicable" or "Not specified in this document" because the submission is not focused on proving clinical effectiveness through a new clinical trial, but rather on demonstrating equivalence based on existing device classifications and non-clinical software testing.

Here's the breakdown based on the request and the provided text:

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

The document does not specify performance acceptance criteria in terms of clinical outcomes (e.g., reduction in tinnitus severity by X%). Instead, the "acceptance criteria" appear to be met by demonstrating substantial equivalence to the predicate devices and by successfully completing software verification and validation.

The closest to "reported device performance" are the technological characteristics listed in the comparison table, which show it operates similarly to the predicates.

Essentially, the "acceptance criteria" here are that the device is demonstrably substantially equivalent to legally marketed predicate devices, and that its software development conforms to relevant standards for safety and performance (i.e., it is well-engineered and functions as intended without introducing new risks).

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

• Not applicable. No clinical test set or patient data for performance evaluation is described in this submission, as "Clinical testing was not performed as part of this submission." The testing referred to is software verification and validation.

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

• Not applicable. As no clinical test set for performance evaluation was involved, no experts were needed to establish ground truth in this context.

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

• Not applicable. As no clinical test set for performance evaluation was involved, no adjudication method was relevant.

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. The document explicitly states "Clinical testing was not performed as part of this submission." Therefore, no MRMC study or assessment of human reader improvement was conducted or reported.

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

• No. The document explicitly states "Clinical testing was not performed as part of this submission." While the device is "software only," its function is to provide customized sounds for a patient to use with the advice of a healthcare professional as part of a management program. Its performance is relative to its ability to play sounds as intended and match the utility of equivalent predicate devices, not as a standalone diagnostic algorithm. The "testing" primarily involved software verification and validation per IEC 62304:2015.

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

• For the software verification and validation testing, the "ground truth" would be the software requirements specifications and design documents. The tests verify that the software functions as designed and meets these specifications. There is no clinical or biological "ground truth" (like pathology or outcomes data) established for device performance in this submission.

8. The sample size for the training set:

• Not applicable. The document does not describe a machine learning model that requires a training set. The "Peace N Quiet" device is described as "software as a medical device" that plays customized sounds. It's not presented as an AI/ML diagnostic or predictive algorithm that learns from data.

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

• Not applicable. As there is no described training set for a machine learning model, the establishment of ground truth for such a set is not relevant.

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