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The WIDE 3MP grayscale TFT LCD monitor IF2103A™is intended to be used in displaying and viewing digital medical images for review and analysis by trained medical practitioners.

The WIDE 3MP grayscale TFT LCD monitor IF2103A™is a flat panel hiresolution LCD monitor system for displaying digital medical images. The system consists of a state-of-the-art LCD monitor and a high-resolution graphic control board that connects to a PACS workstation for grayscale image display. The WIDE controller board is installed into the PACS workstation computer or other computer system used to display PACS medical images.

The provided document is a 510(k) summary for the WIDE 3MP grayscale TFT LCD monitor IF2103A™. This document focuses on demonstrating substantial equivalence to a predicate device for a display monitor, not an AI-powered diagnostic device or a system that performs analysis. Therefore, much of the requested information regarding acceptance criteria for device performance, study designs common for AI/diagnostic devices (e.g., sample sizes for training/test sets, ground truth establishment, MRMC studies, standalone performance), is not applicable to this type of submission.

The "device performance" in this context refers to the technical specifications and safety standards of the monitor itself, ensuring it can display medical images adequately and safely. The acceptance criteria relate to these technical specifications and adherence to recognized standards.

Here's an attempt to answer the questions based on the available information, noting where specific questions are not relevant:

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

The document does not explicitly present a table of acceptance criteria and reported device performance in the format typically seen for algorithms or diagnostic tools. Instead, it refers to compliance with safety and voluntary standards. The "performance" of this device is its ability to display images, which is assumed to meet the requirements of medical image display through its technical specifications and compliance.

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

Not applicable. This device is a display monitor. There is no "test set" of medical images or patient data in the context of diagnostic performance evaluation mentioned in this document. The evaluation focuses on the monitor's technical specifications and compliance with standards.

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)

Not applicable. As this is a display monitor, there is no "ground truth" establishment for diagnostic purposes mentioned in the document.

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

Not applicable. No test set of medical images for diagnostic purposes is discussed.

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 monitor, 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 is a display monitor, not an algorithm, and does not operate in a standalone diagnostic capacity.

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

Not applicable. Ground truth for diagnostic accuracy is not relevant for a display monitor. The "ground truth" for the monitor's function is its technical specifications (e.g., resolution, luminance, uniformity) and adherence to industry standards, which are evaluated through engineering tests, not clinical "ground truth" based on patient data.

8. The sample size for the training set

Not applicable. This device is a display monitor, not an AI model that requires a training set.

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

Not applicable. As there is no training set for an AI model, ground truth establishment is not relevant.

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