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The NOVA is an electrophysiological device that generates photic stimuli, and records, processes and analyzes the resultant visual evoked potential (VEP) signals to provide information about the visual system structural and neural abnormalities.

Checkerboard, Horizontal grating, Vertical grating, Sinusoidal grating, Flash, monochromatic pattern onset or color pattern onset Contrast Photic stimuli are presented to the patient on a calibrated computer monitor at various numbers of elements in separately stimulated fields. The fields are varied in spatial size over a number of cycles. The fields are also phase reversed at different temporal frequencies. The signals are analyzed by the software algorithm for spatial/temporal filtering and artifact rejection. Data may be presented in numerical and graphical form. The device also utilizes attention grabbing features specifically for children or non attentive adults. In particular, a picture is presented prior to the onset of the VEP pattern stimulus. During the picture presentation no data is collected. Age appropriate music is also available to patient as well. The music is only intended as an attention facilitator. From a hardware standpoint the NOVA system is identical to that of the Enfant, ® which was cleared under K043491. The only difference between the two devices is the software.

The provided text describes the Diopsys™ NOVA VEP Vision Testing System, focusing on its substantial equivalence to predicate devices rather than proving its performance against specific acceptance criteria through a clinical study. The performance data section refers to verification and validation activities, which are internal engineering tests, not clinical efficacy studies.

Therefore, much of the requested information regarding acceptance criteria, sample size for test sets, data provenance, expert ground truth, adjudication methods, MRMC studies, standalone performance, and training set details cannot be extracted from the given document.

However, I can provide what is available:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly state "acceptance criteria" for clinical performance. Instead, it lists various verification and validation activities for the software and hardware. The "reported device performance" in this context refers to the successful completion of these internal tests.

2. Sample size used for the test set and the data provenance:

• The document describes verification and validation activities, which typically involve internal testing of the device and software, not a clinical "test set" with a specified sample size of patient data.
• Data Provenance: Not applicable for these internal engineering tests. No information about country of origin or retrospective/prospective nature of patient data is provided.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable. The performance data pertains to internal engineering verification and validation, not clinical ground truth established by experts.

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

• Not applicable. No clinical test set requiring expert adjudication is mentioned.

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:

• No MRMC or comparative effectiveness study is mentioned. The device is a diagnostic tool (VEP system), and the submission focuses on its substantial equivalence to predicate VEP systems, not its impact on human reader performance.

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

• The "Performance Data" section describes software verification and validation, including "Comparison of EEG Response Verifies VEP recording compared to known recording." This, along with "Signal Test Procedure VEP recordings are displayed as expected," suggests standalone performance of the algorithm in processing and displaying VEP data. However, it's not a clinical standalone performance study in the context of diagnostic accuracy against a ground truth. It's more about the algorithm functioning as intended with known inputs.

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

• For the verification and validation (V&V) activities described, the "ground truth" is primarily based on known specifications, expected behaviors, and known recordings. For example, "Verifies VEP stimulus parameters meet specification" and "Verifies VEP recording compared to known recording." This is not clinical ground truth.

8. The sample size for the training set:

• The document does not mention any training data or training set for the device's software/algorithms. This is common for VEP systems, which often use established signal processing techniques rather than machine learning models requiring extensive training data.

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

• Not applicable, as no training set is mentioned.

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