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The Nio Color 2MP LED Medical Flat Panel Display System is intended to be used for displaying and viewing digital images (excluding digital mammography) for review and analysis by trained medical practitioners.
The display may be used in dental applications.

The MDNC-2123 is a derivative of the MDNC-3421. The modifications are:
✓ Change in LCD panel: INX 2MP panel instead of PSD 3MP panel
✓ Change in chassis housing (light-weight design)
✓ Change in packaging (smaller size)
✓ Change in electronics board
✓ Updated firmware

The document provided describes the Barco Nio Color 2MP (MDNC-2123) medical display system and its substantial equivalence to a predicate device (Nio Color 3MP, MDNC-3421). This submission does not involve an AI/ML device, but rather a medical display. Therefore, many of the requested criteria related to AI/ML device studies (such as sample sizes for test/training sets, ground truth establishment by experts, adjudication methods, and MRMC studies) are not applicable.

Here's an analysis based on the information provided, focusing on what is applicable to a medical display device's validation:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for a medical display device typically involve meeting performance specifications relevant to image quality and safety. The document focuses on showing substantial equivalence to a predicate device by comparing technical specifications and performance during bench testing.

The acceptance criteria are implicitly that the differences in technological characteristics of the new device (Nio Color 2MP) do not negatively affect safety or effectiveness compared to the predicate device (Nio Color 3MP) for the stated intended use. Bench testing was performed to demonstrate this.

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

• Test Set Sample Size: Not applicable. For a medical display device, the evaluation involves bench testing and comparison of technical specifications, not a test set of patient data cases in the way an AI/ML diagnostic algorithm would.
• Data Provenance: Not applicable. The validation involves objective performance measurements of the display hardware and software, not clinical data from patients.

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

• Not applicable. "Ground truth" in the context of medical displays refers to objective performance metrics (e.g., luminance, resolution, uniformity) measured against industry standards (like DICOM Part 14) and functional requirements, not expert annotations of medical images. These measurements are typically performed by engineers or technicians using specialized equipment.

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

• Not applicable. Adjudication methods are relevant for resolving discrepancies in expert interpretations of medical images, which is not part of a medical display's validation as 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:

• Not applicable. This device is a display, not an AI/ML algorithm. MRMC studies are used to evaluate diagnostic performance of AI or human readers.

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

• Not applicable. This device is a medical display, which is a display hardware and firmware system. It does not perform diagnostic algorithms in a standalone capacity.

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

• For this device (a medical display), the "ground truth" or reference for performance evaluation would be objective physical and electrical measurements of display characteristics (e.g., luminance, contrast, uniformity, resolution) against established technical standards (e.g., DICOM Part 14 for grayscale display function, IEC standards for electrical safety and EMC).

8. The sample size for the training set:

• Not applicable. This is not an AI/ML algorithm requiring a training set.

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

• Not applicable. This is not an AI/ML algorithm requiring a training set.

Summary of the Study and Conclusion:

The study performed for the Barco Nio Color 2MP (MDNC-2123) was a bench testing and technical specification comparison to demonstrate substantial equivalence to its predicate device, the Nio Color 3MP (MDNC-3421). The justification for substantial equivalence was based on:

• The devices having the same intended use.
• The technological differences (e.g., different LCD panel, chassis, electronics board, firmware, and resulting changes in resolution, screen size, luminance, etc.) not affecting safety or effectiveness for the intended use.
• Bench testing (PPVR for LCD panel, environmental tests for housing/packaging/electronics, electrical safety, EMC, and firmware tests) confirming that the new device has similar characteristics and introduces no new safety or performance issues.

The acceptance criteria were met by demonstrating that despite the technological variations, the device's performance aligns with safety and effectiveness requirements for medical display systems, and that it is suitable for its stated Indications for Use.

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