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The 3MP Color LCD Monitors C32S+, C32SP+ are indicated for use in displaying radiological images for review, analysis, and diagnosis by trained medical practitioners. The displays are not intended for mammography.

The 3MP Monochrome LCD Monitors G32S+, G32SP+ are intended to be used in displaying and viewing digital images for diagnosis of X-ray or MRI, etc. by trained medical practitioners. The devices are not specified for digital mammography systems.

3MP Color LCD monitors C32S+, C32SP+; 3MP Monochrome LCD monitors G32S+, G32SP+ are specifically designed to provide the high definition image outputs for general Radiography.

The products have been strictly calibrated so they meet DICOM Part 3.14 and other standards. They use the latest generation of LED backlight panels, supporting resolution 2048 x 1536. The built-in brightness stabilization control circuits make sure the brightness of these monitors are stable in their life and the calibrations are continuous, so the products meet the demands of high precision medical imaging. For the C32SP+ and G32SP+ surface protection panels with anti-reflection coating, there are characteristics such as anti-reflection, easy cleaning and anti-scratch screen.

Model variations are distinguished by characters. C means the Color monitor, G means the Monochrome monitor, and P means the monitor with an additional glass screen. For example, C32S+ is a color LCD monitor; G32SP+ is a monochrome LCD monitor with the additional glass screen.

The provided document is a 510(k) Pre-Market Notification for medical display monitors (3MP Color LCD Monitors C32S+, C32SP+ and 3MP Monochrome LCD Monitors G32S+, G32SP+). It focuses on demonstrating substantial equivalence to predicate devices rather than providing a study proving performance against specific clinical acceptance criteria for an AI-powered device.

Therefore, many of the requested items (e.g., sample size for test set, data provenance, number of experts, adjudication method, MRMC study, standalone performance, ground truth types and training set details) are not applicable or not present in this type of submission. This document describes the technical specifications and bench test results of the monitors themselves, not the performance of an AI algorithm based on image interpretation.

Here's an analysis based on the information available:

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

The document does not explicitly state "acceptance criteria" in a pass/fail table format for an AI algorithm's performance. Instead, it lists technical specifications of the proposed monitors and compares them to predicate devices, along with bench test results to verify compliance with display standards. The performance reported relates to display characteristics, not diagnostic accuracy of an AI.

However, we can infer some "acceptance criteria" based on the comparison tables and testing performed, which are primarily related to meeting technical standards for medical displays.

Note: Specific numerical results for each measurement (e.g., exact luminance non-uniformity values) are not provided in this summary, but the document states they were measured and found to be equivalent to predicate devices.

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

This information is not applicable as the document describes tests on the physical display monitors, not an AI algorithm processing medical images. The "test set" here refers to the monitors themselves.

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)

This information is not applicable. The "ground truth" for these display monitors is defined by technical specifications and adherence to standards like DICOM and AAPM TG18 guidelines, measured by equipment and engineering assessments, not by expert medical image interpretation.

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

This information is not applicable. Adjudication methods are relevant for clinical studies involving human interpretation or AI output, not for technical verification of display monitors.

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

This information is not applicable. This document is for medical display monitors and does not involve AI assistance or MRMC studies for diagnostic performance improvement.

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

This information is not applicable. No algorithm is being evaluated in this submission.

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

The "ground truth" for the performance evaluation of these medical displays is established by adherence to technical and performance standards for display devices, such as DICOM Part 3.14 and AAPM Task Group 18 (TG18 guideline) for display performance. It's measured through physical bench testing and comparison to established engineering specifications and predicate devices.

8. The sample size for the training set

This information is not applicable. There is no AI algorithm with a training set being described in this document.

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

This information is not applicable. There is no AI algorithm with a training set being described in this document.

In summary, the provided text is a 510(k) summary for medical display monitors, focusing on their technical specifications and substantial equivalence to predicate devices, as demonstrated through bench testing against established display standards. It does not contain information related to AI algorithm performance or clinical studies of diagnostic accuracy.

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