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The GSI Novus is intended to be used for the measurement and automated analysis of auditory evoked responses (auditory brainstem responses, ABR) and/or otoacoustic emissions (distortion product, DPOAE and transient evoked, TEOAE). These measures are useful in the screening evaluation, documentation and diagnosis of auditory and hearing related disorders. The auditory evoked response (ABR) measurement is intended for newborns and infants up to 6 months of age. The otoacoustic emissions (DPOAE and/or TEOAE) measurement is intended for use in patients of all ages.

The GSI Novus is intended to be used by a healthcare professional such as an ENT doctor, nurse or audiologist or by a trained technician under the supervision of a professional. The device is intended to be used in a hospital, clinic, or other facility with a suitable quiet testing environment.

The device is audiometric equipment used for testing of inner ear and auditory brainstem abnormalities.

Novus features a touch-screen display and user-friendly software in a compact hardware design. Novus can be purchased with various licenses allowing you to perform different hearing screening tests.

Novus uses auditory brainstem response (ABR) technology to screen patients for hearing loss. A modified click stimulus, the CE-Chirp", of 35 dB nHL is delivered into the patient's ear while electrodes placed on the patient's head measure EEG activity.

The EEG is processed and analyzed automatically using the Novus's response detection algorithm. When a response is detected, the screening is stopped automatically and a Pass result is assigned to the test ear. When no response is detected after 3 minutes of EEG activity has been processed, a Refer result is assigned.

Auditory brainstem response (ABR) test produces a short acoustic stimulus and measures via transcutaneous electrodes the auditory evoked potentials from the inner ear, the auditory nerve and the brainstem.

Distortion product otoacoustic emissions (DPOAE) technology uses pairs of pure tones presented in sequence to screen patients for cochlear hearing loss. Responses to the stimulus are predictable and therefore can be measured via a sensitive microphone placed in the patient's ear canal.

Transient otoacoustic emissions (TEOAE) technology uses a click stimulus to screen patients for cochlear hearing loss. Responses to the stimulus are predictable and therefore can be measured via a sensitive microphone placed in the patient's ear canal. The response can be divided into frequency bands for assessment.

The Novus consists of a handheld unit that utilizes a touchscreen display and a rechargeable battery. A simple cradle is included to support charging of the device's battery. The device supports Bluetooth® communication with a label printer for the purpose of printing screening results.

This document does not contain an acceptance criteria table or a study proving the device meets specific acceptance criteria in the format requested. The document is a 510(k) premarket notification letter from the FDA to Grason-Stadler Inc. for their GSI Novus device. It primarily focuses on demonstrating substantial equivalence to predicate devices rather than presenting detailed clinical trial results or specific performance metrics against pre-defined acceptance criteria.

However, based on the provided text, I can extract information related to the device's intended use, general performance claims, and the type of non-clinical testing performed to support its safety and effectiveness.

Here's an attempt to answer your questions based only on the provided text, noting where information is explicitly not present:

Acceptance Criteria and Device Performance Study for GSI Novus (K172403)

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

The document does not provide a table of explicit acceptance criteria with corresponding device performance metrics. Instead, it asserts that the GSI Novus meets performance specifications by complying with international standards and through non-clinical design verification and validation. The "reported device performance" is primarily qualitative, stating that the device is "safe and effective" and that its performance characteristics are comparable to predicate devices.

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

The document explicitly states: "No clinical tests were performed" and "Summary of Clinical Testing: Not applicable. Not required to establish substantial equivalence." Therefore, there is no test set sample size, data provenance, or information on retrospective/prospective studies from clinical testing. The "data collection" mentioned in Phase 2 refers to data collected during non-clinical verification and validation activities.

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)

Since no clinical tests were performed and no "test set" in the context of human data was used to establish ground truth for clinical performance, this information is not applicable and not provided.

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

Not applicable, as no clinical test set was used for ground truth establishment.

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

Not applicable. The document states "No clinical tests were performed." Furthermore, the GSI Novus is described as an auditory testing device with an automatic response detection algorithm ("Novus's response detection algorithm"), implying it functions as a standalone diagnostic aid rather than an AI assistance tool for human "readers" (in the typical sense of image interpretation).

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

Yes, the device relies on an automated analysis algorithm. The description mentions:

• "The EEG is processed and analyzed automatically using the Novus's response detection algorithm."
• "When a response is detected, the screening is stopped automatically and a Pass result is assigned to the test ear. When no response is detected after 3 minutes of EEG activity has been processed, a Refer result is assigned."
• "The detailed information about the validation and verification of PASS/REFER algorithms for the OAE and ABR modules is provided in the GSI Novus Manual, e.g., PASS/REFER Criteria, Sensitivity and Specificity etc."

This indicates that a standalone algorithm performance was assessed for the automated PASS/REFER results during the non-clinical design verification and validation activities, although specific performance metrics (like sensitivity, specificity) are referenced as being in the manual but not provided in this document.

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

For the non-clinical verification and validation of the "PASS/REFER algorithms," the "ground truth" would have been established by comparing the algorithm's output to expected or reference values derived from recognized audiological principles and potentially a "gold standard" reference measurement or simulation. The document mentions "Phase 3 then went into the algorithm descriptions for each TEOAE, DPOAE and ABRIS measurements modes," implying that the ground truth for validating the algorithms themselves would be based on established audiological standards and the device's ability to accurately detect "responses" or "no responses" against these standards. Specifics of how this "ground truth" was established (e.g., against a known simulated signal, against a predicate device's output, or against expert analysis of raw data) are not detailed here.

8. The sample size for the training set

The document does not mention a "training set" or "training data" in the context of machine learning, nor does it specify any sample size for such a set. Given the context of a 510(k) for an audiometer with an automated algorithm, it's possible the algorithm logic was developed based on established audiological principles and signal processing, rather than a large-scale data training approach commonly associated with AI/ML.

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

Not applicable, as no training set is described or referenced in the document.

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