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The Nio 3MP Medical Grayscale Display System is intended to be used as a tool in displaying and viewing digital images (excluding digital mammography) for review and analysis by trained medical practitioners.

The Nio 3MP device is a digital image display system. The Barco Nio 3MP device consists of components to provide high resolution visualization of digital images.

The provided text is a 510(k) summary for the Barco Nio 3MP Medical Grayscale Display System, which is an image display system. The information primarily concerns its regulatory clearance based on substantial equivalence to a predicate device, rather than a clinical study evaluating its performance against specific acceptance criteria for diagnostic efficacy.

Therefore, many of the requested details about acceptance criteria, study design, and performance metrics are not available in the provided text. The device is a display system, not an AI or diagnostic tool that would typically have the kind of performance metrics you're asking for.

Here's what can be extracted based on the provided text:

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

The document does not explicitly state quantitative acceptance criteria or device performance figures in the way you would expect for a diagnostic medical device (e.g., sensitivity, specificity, accuracy). The acceptance is based on demonstrating "substantial equivalence" to a predicate device.

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

Not applicable. This is a display system, not a diagnostic algorithm. The 510(k) process for a display system typically involves technical specifications and performance validation (e.g., luminance, uniformity, resolution) rather than clinical studies with patient data.

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

Not applicable. Ground truth establishment with experts is relevant for diagnostic devices that interpret medical images. This is a display device.

4. Adjudication method for the test set

Not applicable. Adjudication methods are used in studies involving human interpretation or AI performance assessment.

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. This is a display device, not an AI-assisted diagnostic tool.

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

Not applicable. This device is a display, not an algorithm. Its function is to facilitate human review.

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

Not applicable. The concept of "ground truth" for a display device would relate to its technical specifications (e.g., accurate color representation, resolution, contrast), which are typically verified through engineering tests, not clinical ground truth.

8. The sample size for the training set

Not applicable. Training sets are used for machine learning algorithms.

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

Not applicable. Training sets and ground truth for them are relevant to machine learning.

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