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CL-S200 (CL-S200xxxxx) and CL-S300 (CL-S300xxxxx) are intended to be used in displaying and viewing medical images for diagnosis by trained medical practitioners or certified personnel. It is not meant to be used in digital mammography.

CL-S200 : 1600 x 1200 (landscape), 1200 x 1600 (portrait)
CL-S300 : 2048 x 1536 (landscape), 1536 x 2048 (portrait)
DVI (digital interface) and DisplayPort (digital interface)

The provided text describes the acceptance criteria and performance of medical LCD monitors (CL-S200 and CL-S300) for displaying and viewing medical images. It's a 510(k) summary for these devices, asserting their substantial equivalence to a predicate device.

Here's an analysis of the acceptance criteria and whether the document describes a study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The document provides a "Technical Specification" section that lists acceptance criteria (referred to as [SPEC]) and then compares the new devices (CL-S200xxxxx, CL-S300xxxxx) against a predicate device (CCL214/[K161895](https://510k.innolitics.com/search/K161895)) in a "Substantial Equivalence Comparison" table. This comparison implicitly shows the reported device performance.

Important Note: The provided document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than providing detailed results from a new comprehensive performance study. The "performance" listed (e.g., Maximum Luminance, Contrast Ratio) are device specifications and comparisons to the predicate, not explicitly "reported device performance" from a dedicated study proving they meet each [SPEC] criterion with actual measured values. The assertion that they "share the same technical characteristics" is the primary argument for meeting the criteria.

2. Sample Size Used for the Test Set and the Data Provenance

This document describes medical display monitors, not an AI or diagnostic algorithm, so there is no "test set" of images or patient data in the typical sense. The performance criteria relate to the physical and optical properties of the display.

• Sample Size: Not applicable in the context of image data. The "sample size" would refer to the number of monitors tested. The document does not specify the number of monitors tested for these technical specifications.
• Data Provenance: Not applicable to image data. The technical specifications are inherent to the monitor design and manufacturing.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts

Not applicable. Ground truth for display monitor performance is established by objective physical measurements (e.g., luminance meters, colorimeters, spectroradiometers) according to specified standards like AAPM-TG18 and ISO13406-2, not by expert consensus on medical images.

4. Adjudication Method for the Test Set

Not applicable. There is no human interpretation of a test set requiring adjudication for these medical 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

Not applicable. This document is about medical display monitors, not an AI or diagnostic algorithm that assists human readers.

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

Not applicable. This document is about medical display monitors, which are passive display devices, not standalone algorithms.

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

The ground truth for the "acceptance criteria" (technical specifications) of these monitors is based on objective physical measurements using specialized equipment and established industry standards (e.g., AAPM-TG18, ISO13406-2) for display performance.

8. The sample size for the training set

Not applicable. This is not an AI/ML device.

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

Not applicable. This is not an AI/ML device.

In Summary:

The provided document details a 510(k) submission for medical display monitors. Its primary goal is to demonstrate "substantial equivalence" to existing legally marketed predicate devices, not to present a comprehensive, independent study proving performance against each specific acceptance criterion with detailed, quantitative results from a large test set (which would be typical for an AI algorithm). The implicit proof of meeting acceptance criteria comes from the assertion that the new devices "share the same technical characteristics, application, and intended use" as the predicate device, and by listing their general specifications. The "acceptance criteria" are technical specifications related to display quality, and "proof" comes from internal testing and compliance with display standards, rather than clinical studies with patient data and expert ground truth.

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