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The indication for use of the air conduction hearing aids in this submission is to amplify sound for individuals with impaired hearing. The devices are indicated for individuals with losses in the following category(ies). (Check appropriate space(s)):
Severity:
✓ 1. Slight
✓ 2. Mild
✓ 3. Moderate
✓ 4. Severe
Configuration:
✓ 1. High Frequency Precipitously Sloping
✓ 2. Gradually Sloping
✓ 3. Reverse Slope
✓ 4. Flat
✓ 5. Other
Other:
✓ 1. Low tolerance to Loudness

Behind-the-Ear (BTE) Hearing Aid

The provided text introduces the Miracle-Ear Model DBS Hearing Aid and states that its technical characteristics are assessed using the ANSI S3.22-1987 methodology. It also mentions that the device is substantially equivalent to the Oticon Digifocus Compact BTE, which was cleared under 510(k) # K961059. However, the document does not provide acceptance criteria or details of a study that proves the device meets specific performance criteria. The FDA letter confirms the device's substantial equivalence but does not describe the specific tests or results that led to that determination.

Therefore, many of the requested details cannot be extracted from this document.

Here's an attempt to answer the questions based on the limited information:

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

The document states that "Dahlberg utilizes the ANSI S3.22-1987 methodology to test and obtain performance data for hearing aids." This implies that the acceptance criteria would be defined by this standard, and the device's performance would be measured against it. However, the specific acceptance criteria values (e.g., minimum gain, maximum output, harmonic distortion limits from ANSI S3.22-1987) and the actual reported device performance values are not provided in this document.

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

This information is not provided in the document. The document refers to "performance data" but does not specify the sample size, data origin, or study design (retrospective/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 applicable as the document describes a hearing aid, not a diagnostic imaging device requiring expert interpretation for ground truth establishment. The performance testing for hearing aids typically involves objective acoustical measurements against a standard.

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

This information is not applicable for the type of device and performance testing described. Adjudication is not typically used for objective acoustical measurements of hearing aid performance.

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

This information is not applicable as the device is a hearing aid and not an AI-powered diagnostic tool used by human readers for interpretation.

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

This information is not applicable as the device is a hearing aid. Its "standalone performance" refers to its acoustical output, which would be measured directly against the standard, not an algorithm's performance.

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

For a hearing aid, the "ground truth" for performance would be objective acoustical measurements obtained by following the methodology outlined in the ANSI S3.22-1987 standard. This standard prescribes how to measure various performance characteristics such as frequency response, full-on gain, maximum output (OSPL90), and harmonic distortion.

8. The sample size for the training set

This information is not applicable. Hearing aids are physical devices, and their performance is typically evaluated through direct measurement and engineering specifications rather than a "training set" used for machine learning.

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

This information is not applicable for the reasons stated in point 8.

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