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19-inch (48cm) 1.3M Monochrome LCD Monitor MDL1908A is to be used in displaying and viewing medical images for diagnosis by trained medical practitioners. It is not meant to be used in digital mammography.

MDL1908A is a 19-inch (48cm) 1.3 megapixel Monochrome LCD monitor that supports DVI video signal and provides UXGA (1280 x 1024) resolution for both landscape and portrait display.

The provided text describes a 510(k) submission for a medical display monitor, not a device that processes or analyzes medical images using AI or other algorithms. Therefore, many of the requested criteria such as expert consensus, ground truth, training/test sets, and AI assistance are not applicable to this type of device.

This document focuses on establishing substantial equivalence to a predicate device (another medical monitor) based on physical and performance characteristics typically assessed for displays.

Here's a breakdown of the relevant information from the provided text, and an explanation of why other criteria are not applicable:

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

The document doesn't explicitly state "acceptance criteria" in a tabulated format for direct comparison like a diagnostic device might. Instead, it implies that the device meets the performance characteristics expected of a 1.3M Monochrome LCD monitor for diagnostic medical image viewing, and that these characteristics are substantially equivalent to its predicate device.

The characteristics of the device itself are:

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 is a display monitor, not a diagnostic algorithm or device that analyzes patient data. There is no "test set" of patient data in the context of conventional diagnostic testing for this device. The evaluation would involve technical performance tests of the monitor itself (e.g., brightness, contrast, uniformity, resolution, color accuracy if applicable for color monitors, etc.), not a set of medical images for diagnostic accuracy.

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. There is no "ground truth" to establish for a display monitor in the same way there would be for a diagnostic tool. The ground truth for a display is its adherence to technical specifications and display standards (e.g., DICOM Part 14 Grayscale Standard Display Function).

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

Not applicable, for the same reasons as points 2 and 3.

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 device is not an AI-assisted diagnostic tool. No MRMC study would be performed for a medical display monitor in this context. Its purpose is to accurately display images, not to assist in interpreting them directly or to perform AI analysis.

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

Not applicable. This device is purely a display monitor; there is no embedded algorithm for standalone performance.

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

Not applicable. The "ground truth" for a monitor would be its technical specifications and compliance with relevant industry standards (e.g., DICOM Part 14 for grayscale display functions, NEMA display requirements).

8. The sample size for the training set

Not applicable. This device does not use a training set as it's a hardware display and not a learning algorithm.

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

Not applicable. (See point 8).

In summary, the 510(k) for a medical display monitor like the MDL1908A focuses on demonstrating that the device's technical specifications and performance are adequate for its intended use (displaying medical images for diagnosis) and that it is substantially equivalent to a legally marketed predicate device. The criteria typically associated with diagnostic algorithms (AI, ground truth, reader studies) are not relevant here.

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