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510(k) Data Aggregation
(61 days)
QUIESCENCE
This software device, Quiescence™, is indicated for use by otolaryngologists, and hearing-aid dispensers (collectively referred to as healthcare professionals) for generating custom masking sounds to mask timitus as part of a tinnitus management program. The target population of the device is adults (18 years and over) who report experiencing timitus, which may or may not be accompanied with hearing loss. Patients should receive a medical evaluation by a licensed physician who specializes in diseases of the ear to rule-out medically or surgically reatable diseases for which tinaitus is a symptom before proceeding with non-medical tinnitus management.
Quiescence provides a means for healthcare professionals to custom-make masking sounds for tinnitus patients. This software allows the healthcare professional to try different masking sounds on the patients until one or more suitable ones are found. The masking sounds will be stored to the computer hard disk. This allows the stored masking sound to be written or recorded into other convenient formats such as CD and MP3 player for playback.
The Quiescence Software (K040330) is a tinnitus masker. The information provided in the 510(k) summary focuses on comparing the device to a predicate device (TinniTech ANMP System, K030791) and discussing its technical specifications and intended use. The provided document does not contain explicit acceptance criteria in a quantitative format, nor does it detail a clinical study with a test set, expert ground truth establishment, or sample sizes typically associated with performance evaluations of AI/ML-based medical devices.
Instead, the submission relies on a "brief discussion and conclusion of non-clinical tests and their results" to demonstrate the device's accuracy in producing masking sounds.
Here's an attempt to structure the available information regarding acceptance criteria and the "study":
1. Table of Acceptance Criteria and Reported Device Performance
Based on the provided text, the acceptance criteria are implicitly related to the accuracy of the generated masking sounds. The performance is reported as meeting these implicit criteria.
| Acceptance Criteria (Implicit) | Reported Device Performance |
|---|---|
| The masking sound produced by the Quiescence software should match the intended output. | "The results indicated that all the masking sounds generated matched the intended masking sound output." |
| The algorithms generating the masking sound should be analyzed. | "The algorithms that generate the masking sound were analysed..." |
| The generated sound should be analyzed before and after passing through the sound output device (headset). | "...and the generated sound was also analysed before and after it passed through the sound output device (headset)." |
| Spectrum analysis should be used to ensure the proper frequency content in the masking sound. | "When analyzing the masking sound, spectrum analysis was used to ensure the proper frequency content in the masking sound." |
| The generated masking sound should be as effective as what other masking devices would generate (implied by substantial equivalence claim). | "This clearly indicates the masking sound generated by the Quiescence software will be as effective as what other masking devices would generate." (This is a conclusion drawn from the technical accuracy, not a direct clinical effectiveness study against other devices in this document). |
| The device must include warnings/alerts for output levels exceeding user-defined limits (default 85 dBA, alert at 80 dB SPL) to prevent hearing damage. | "The software will also display a warning message to alert healthcare professional when the output is going to exceed a user-defined limit. The default limit of the software ensures that the output is below 85 dBA." |
| User's guide must include permissible noise exposure duration and corresponding noise level. | "The user's guide of Quiescence includes the list of permissible noise exposure duration and the corresponding noise level." |
| Final recording of masking sounds must include specific warning labels as proposed. | "The following warning label is proposed to be included with the recording of the masking sounds generated by Quiescence:" [Warning text provided] |
| Healthcare professionals should provide adequate instruction to the patient. Appendix A of the User's Guide should contain cautionary and usage information for patients. | "Furthermore, the healthcare professional should provide the patient with adequate instruction to use the masking sound(s) at home. Appendix A of the Quiescence User's Guide contains cautionary and usage information for tinnitus patients that healthcare professionals should distribute..." |
2. Sample Size Used for the Test Set and Data Provenance
The provided document describes non-clinical engineering tests, not a clinical study involving patients or a test set of patient data. Therefore, the concepts of "sample size for the test set" and "data provenance" (country of origin, retrospective/prospective) are not applicable in the context of this device's submission as described. The testing focused on the device's technical output accuracy.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of those Experts
Not applicable. This was a non-clinical engineering test of sound output accuracy, not a clinical evaluation requiring expert-established ground truth on patient data. The "ground truth" was the intended sound output specifications.
4. Adjudication Method for the Test Set
Not applicable. No clinical test set or human adjudication process is described.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done
No. An MRMC comparative effectiveness study was not performed or referenced in the provided text. The submission focuses on demonstrating substantial equivalence to a predicate device based on intended use, technology, and non-clinical performance (accuracy of sound generation).
6. If a Standalone Performance Study (Algorithm Only Without Human-in-the-Loop Performance) was Done
Yes, in a way. The "brief discussion and conclusion of non-clinical tests" can be considered a standalone performance evaluation of the algorithm's output, without direct human-in-the-loop performance influencing the sound generation itself. The software's ability to produce the correct sounds according to its design specifications was tested.
7. The Type of Ground Truth Used
The ground truth used was technical specifications/intended output parameters for the generated masking sounds. The tests aimed to verify that the software's output (frequency content, etc.) matched these predefined engineering targets.
8. The Sample Size for the Training Set
Not applicable. This device is described as software that "creates custom masking sounds" using "algorithms." There is no indication that it is an Machine Learning (ML) algorithm that requires a "training set" of data in the conventional sense. It appears to be a deterministic sound generation software.
9. How the Ground Truth for the Training Set was Established
Not applicable, as there is no apparent training set for an ML algorithm. The "ground truth" for the software's functionality resides in its design specifications for generating various types of sounds (pure tone, FM tone, etc.) with specific parameters.
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