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The device is intended to be used to as a tool in displaying and viewing digital images view and analyses by trained medical practitioner. The device is not specified for digital mammography system.

The MMD-3213M LCD monitors are displays for medical image viewing. MMD-3213M provides 3 mega pixel resolution. The PACSmate MMD-3213M is a high performance, 3 Megapixel medical grade monochrome LCD monitors designed for exacting needs for diagnostics professionals that provide clear and sharp images with resolutions of up to 2048 x 1536 pixels, 600 cd/m2 brightness and 700:1 contrast ratio, making it ideal for diagnosing detailed medical graphics. Also uses a DVI digital interface offering compatibility with the latest digital standards.

The provided text describes a 510(k) premarket notification for a medical display device, the PACSmate MMD-3213M monitor. However, it does not contain the detailed performance study information, acceptance criteria, or ground truth establishment relevant to the questions asked.

The document primarily focuses on demonstrating substantial equivalence to a predicate device (EIZO RadiForce GS320) based on intended use, construction, function, safety, operating environmental conditions, and effectiveness. It mentions "Performance Testing" but only states: "The performance test results for the PACSmate LCD monitor demonstrated that the device meets its intended use specifications and therefore meets the requirements necessary for its intended use as a displayer for medical image." It does not provide the specific acceptance criteria or the actual results of these performance tests.

Therefore, I cannot populate the table or answer most of your specific questions based on the provided text.

Here's what can be extracted and what cannot:

1. Table of acceptance criteria and the reported device performance:

• Cannot be provided from the text. The document states "device meets its intended use specifications" but does not define these specifications or provide quantitative performance metrics.

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

• Cannot be provided from the text. There is no mention of a "test set" in the context of image analysis or diagnostic performance. The performance testing likely refers to technical specifications of the monitor itself (e.g., brightness, contrast, resolution) rather than clinical image interpretation studies.

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

• Cannot be provided from the text. No ground truth establishment is described, as the device is a display monitor, not an AI diagnostic algorithm.

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

• Cannot be provided from the text. Not applicable for a display monitor's performance testing described here.

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:

• Cannot be provided from the text. Not applicable, as this is a medical display, not an AI-powered diagnostic tool. The document focuses on the monitor's technical compliance and substantial equivalence.

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

• Cannot be provided from the text. Not applicable; it's a monitor.

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

• Cannot be provided from the text. Not applicable.

8. The sample size for the training set:

• Cannot be provided from the text. Not applicable; this is a display monitor, not an AI algorithm.

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

• Cannot be provided from the text. Not applicable.

In summary, the provided document describes the regulatory approval of a medical display monitor based on its technical specifications and substantial equivalence to a predicate device. It does not include information about clinical studies, diagnostic performance, expert review, or ground truth establishment, which are typical for AI-based diagnostic devices.

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