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The CST system is a CD with software that enables a qualified healthcare professional to identify, with the patient's verbal input, the sounds that most closely match the patient's tinnitus. The device is indicated to mask and intermittently mask tinnitus as part of a tinnitus management program. The target population for the device is adults (18 years and over) who present with timitus, that may or may not be accompanied with hearing loss at the higher frequencies, and who are participating in a tinnitus management program.

The Tinnitus Otosound Products LLC (TOP-LLC), Customized Sound Therapy (CST), falls under devices described in 21 CFR 874.3400 Class II, product code KLW. The device is a CD comprised of software with two components: a graphic user interface and cmusic program. The software can be used on a notebook or desktop computer with at least Windows XP (SP2) having at least a 1.2 GHz Pentium III CPU (or equivalent), 256 MB of RAM, 1 GB of free disk space, a CD drive, and an available USB port.

The Customized Sound Therapy software produces, and transfers sounds to a sound wave file. This sound file can be stored on any commercially available computer hard drive or portable audio device (PAD) like an iPod.

The provided text is a 510(k) summary for the "Customized Sound Therapy (CST)" device, a Tinnitus Masker Device. It aims to establish substantial equivalence to predicate devices rather than proving performance against specific acceptance criteria for a new clinical claim. Therefore, much of the requested information regarding detailed acceptance criteria and a study proving those criteria are not explicitly present in the document in the format usually associated with performance studies against pre-defined metrics.

However, I can extract information related to the device's characteristics and the basis for its approval.

Here's a breakdown based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The 510(k) summary does not present specific quantitative acceptance criteria or a performance table in the way a clinical study typically would. The basis for approval is Substantial Equivalence to legally marketed predicate devices. This means the FDA determined that the CST device is as safe and effective as the predicate devices. The "performance" in this context is implicitly considered equivalent to the predicates.

The device's intended use is to "mask or intermittently mask the patient's tinnitus as part of a tinnitus management program."

Warnings regarding playing sounds at uncomfortable levels and not using if it prevents hearing warning sounds of danger are explicitly stated as safety measures. |
| Functional Equivalence | The CST software produces and transfers sounds to a sound wave file, which can be stored on a computer or portable audio device. This function is analogous to the sound-producing and delivery mechanisms of existing tinnitus maskers. |
| Intended Use Equivalence | The intended use of CST is consistent with that of existing tinnitus maskers: to provide temporary relief from the disturbance of tinnitus by masking or intermittently masking it. |
| Technological Characteristics Equivalence | The device is a CD with software that uses a "cmusic acoustic compiler and a proprietary graphic user interface" to create individualized sounds. This technological approach is deemed comparable to the sound generation methods employed by predicate devices. Standard commercially available computer and audio equipment is used. |

2. Sample Size Used for the Test Set and Data Provenance

The 510(k) summary does not mention a specific test set, sample size, or data provenance (country of origin, retrospective/prospective) for a performance study. 510(k) submissions focusing on substantial equivalence often rely on comparative analysis to predicates rather than new clinical data, especially for Class II devices like this one.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

This information is not provided in the document. As there's no mention of a dedicated test set or ground truth establishment for a performance study, the number and qualifications of experts for this purpose are not applicable in this context.

4. Adjudication Method for the Test Set

This information is not provided in the document, as no specific test set requiring adjudication is described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study is not described in the provided 510(k) summary. The document does not discuss human reader performance, AI assistance, or an effect size.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study Was Done

No, a standalone performance study of the algorithm is not explicitly described in the document. The device description emphasizes the role of a "qualified audiologist or other qualified professional" in controlling the software and sound matching with the patient's verbal input. This implies a human-in-the-loop system for creating the personalized sound files, rather than a fully standalone algorithm.

7. The Type of Ground Truth Used

The concept of "ground truth" as typically used in performance studies (e.g., pathology, outcomes data) is not applicable here in the context of proving specific device performance metrics against a gold standard. The "ground truth" for the device's function is the patient's subjective perception of their tinnitus and the effectiveness of the generated sound in masking it, as determined through the interaction with a qualified professional. The professional, with patient input, identifies sounds that "most closely match the patient's tinnitus."

8. The Sample Size for the Training Set

There is no mention of a training set sample size. The software is described as based on the cmusic acoustic compiler and a proprietary graphic user interface, implying it's a tool for sound generation rather than a machine learning model trained on a dataset to make predictions.

9. How the Ground Truth for the Training Set Was Established

Not applicable, as there is no mention of a training set or ground truth establishment for a machine learning model. The software's functionality seems to be based on acoustical principles for sound generation and a user interface for customization, rather than being "trained" on a dataset with established ground truth.

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