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20.8-inch (53cm) Color LCD Monitor CDL2112A (CCL350i) (DV3MC-HB) is to be used in conjunction with the picture archiving communication systems (PACS) for medical imaging applications. It is not meant to be used for digital mammography.

CDL2112A (CCL350i) (DV3MC-HB) is a 20.8-inch Color LCD Monitor that supports DVI video signal and provides QXGA (2048 x 1536) resolution for both landscape and portrait display.

This document describes a 510(k) submission for a medical display monitor, not an AI-powered diagnostic device. Therefore, the requested information about acceptance criteria, study details, and AI-specific metrics (like human reader improvement with AI assistance, training set details, and ground truth establishment for training) is not applicable or cannot be extracted from the provided text.

The document is a 510(k) summary and the FDA's clearance letter for the "20.8-inch (53cm) Color LCD Monitor CDL2112A (CCL350i) (DV3MC-HB)" from TOTOKU ELECTRIC CO., LTD. This is a display device intended for medical imaging applications in conjunction with PACS, not a software algorithm that performs diagnostic analysis.

Here's what can be gathered, addressing the spirit of the request where applicable to a medical display:

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

The document does not explicitly state "acceptance criteria" in the way one would for a diagnostic algorithm (e.g., sensitivity, specificity thresholds). For a medical display, acceptance criteria would typically relate to its technical specifications and performance characteristics, and compliance with relevant standards.

The document mentions:

• Device Name: 20.8-inch (53cm) Color LCD Monitor CDL2112A (CCL350i) (DV3MC-HB)
• Resolution: QXGA (2048 x 1536) for both landscape and portrait display.
• Intended Use: To be used in conjunction with picture archiving communication systems (PACS) for medical imaging applications. Not meant to be used for digital mammography.
• Substantial Equivalence: The device has "almost the same characteristics" as the predicate device CDL2114A (K043430), "except for the molds, the LCD panel, and the front sensor". This implies that the performance characteristics of the predicate device serve as the de facto acceptance criteria. The specific performance metrics of the predicate device are not detailed here but would have been assessed during its own clearance process.

Summary Table (based on available information for a display device):

2. Sample sized 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 hardware device (monitor), not a software algorithm tested on medical image data. There is no "test set" of medical images for software performance evaluation mentioned.

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 above, this pertains to a monitor, not an algorithm requiring ground truth derived from expert interpretation of medical images.

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

Not applicable.

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 is a monitor, not an algorithm.

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

Not applicable. Ground truth as typically understood for diagnostic AI algorithms is not relevant for a medical display device's clearance. The ground truth for a display would relate to its ability to accurately render images according to technical specifications and industry standards.

8. The sample size for the training set

Not applicable. This is a hardware device; there is no "training set" in the context of machine learning.

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

Not applicable.

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