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The Amplaid A311 Series is a two channel clinical diagnostic audiometer which can perform all audiometric tests normally performed in a clinical situation. It is intended for use by a qualified/trained audiologist on both adult and pediatric subjects for measurement and diagnosis of various types of hearing losses.

The Amplaid A311 Series is a two channel clinical diagnostic audiometer which can perform all audiometric tests normally performed in a clinical situation. It provides testing capability for basic evaluation, cochlear versus retrocochlear dysfunction, central dysfunction and non-organic hearing loss. Other diagnostic audiometric tests are: pure tone, speech, Multifrequency, S.I.S.I., A.B.L.B. (Fowler), D.L.I. (Lüscher), M.L.B., and Tone Decay. The A311 Series comes in two models, both using the same internal circuit board: The AM11 which uses a personal computer as the user interface (communicates via RS232), and the A311, with uses a dedicated front panel keyboard with an LCD display.

The provided document [0-3] does not contain specific acceptance criteria or a detailed study proving the device meets said criteria in the way typically expected for a medical device efficacy study (e.g., performance metrics like sensitivity, specificity, accuracy against a gold standard).

Instead, this document is a 510(k) summary for the Amplaid A311 Series audiometer seeking substantial equivalence to predicate devices already on the market. The "acceptance criteria" here are primarily based on demonstrating that the new device performs "as safe and effective as the predicate devices" and conforms to relevant medical device standards.

Here's an analysis based on the information provided:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implied by the comparison to predicate devices and adherence to relevant standards. The "reported device performance" is essentially that it meets these standards and functions similarly to the predicate.

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

The document states: "The results of bench and user testing indicates that the new device is as safe and effective as the predicate devices." [0] and "After analyzing both bench and user testing data, it is the conclusion of Amplifon S.p.A. that the 'Amplaid A311 Series™' is as safe and effective as the predicate device..." [1] However, the specific sample sizes, the nature of the "test set" (e.g., number of subjects, type of data collected, number of test runs), or the data provenance (country, retrospective/prospective) are not disclosed in this 510(k) summary. It implies testing was done, but details are absent.

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

This type of information is not provided in the document. For a 510(k) submission focused on substantial equivalence, the "ground truth" for the device's performance is often established by comparing its output to that of the predicate device, or by confirming its adherence to objective performance standards (e.g., accuracy of dB levels, frequency output). Expert consensus for "ground truth" in terms of clinical diagnosis or outcomes is not explicitly mentioned as a component of this submission type for an audiometer.

4. Adjudication Method for the Test Set

This information is not provided. Given the nature of an audiometer, "adjudication" methods like 2+1 or 3+1 consensus, typically used for image or diagnostic interpretation, are not directly applicable. The "user testing" mentioned might involve comparing measurements from the new device against the predicate device or a calibrated standard.

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

No, an MRMC comparative effectiveness study is not mentioned in the document. This type of study is more relevant for diagnostic devices where human readers interpret outputs (e.g., radiologists reading images) and the AI aims to assist or replace that interpretation. An audiometer directly measures hearing thresholds, and its effectiveness is determined by its measurement accuracy and reproducibility, not by how it improves human reader performance in interpreting its output.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

The device itself is a standalone clinical diagnostic audiometer. Its primary function is to generate tones and measure responses, guided by a qualified/trained audiologist. The concept of "algorithm only without human-in-the-loop performance" in the context of an audiometer would likely refer to its ability to generate accurate and consistent sounds and register patient responses correctly, independent of operator skill (though an audiologist uses the device). The document broadly implies standalone performance was assessed through "bench and user testing" to confirm it meets audiometric standards, but it doesn't separate an "algorithm-only" performance from the device's operation by a user.

7. The Type of Ground Truth Used

The "ground truth" for an audiometer's performance typically refers to:

• Measurement Accuracy against Calibrated Standards: The device's ability to output precise frequencies and decibel levels, and to accurately record patient responses, as calibrated against known, accepted reference standards (e.g., conforming to ISO and ANSI audiological standards).
• Comparison to a Predicate Device: Demonstrating that the new device produces equivalent results to an already legally marketed and accepted predicate audiometer.

The document emphasizes adherence to:

• Audiometric Standards: ISO 389-1989, ANSI S3.6-1989, IEC 60645.
• Electrical Safety Standards: UL 2601, IEC 60601-1.

This suggests the ground truth was established by verifying the device's technical specifications and performance against these published standards and against the established performance of the predicate device.

8. The Sample Size for the Training Set

This information is not applicable/provided. Audiometers are not typically "trained" in the way AI algorithms are. Their function is based on established physics and electronics to generate and measure sound. Therefore, there is no "training set" in the context of machine learning.

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

This information is not applicable/provided for the reasons stated above.

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