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MS-S300 is intended to be used in displaying medical images for diagnosis by trained medical practitioners or certified personnel. It's intended to be used in digital mammography PACS, digital breast tomosynthesis and modalities including FFDM.

21.3 inch Monochrome Digital Mammography LCD Monitor
2048 x 1536 (landscape), 1536 x 2048 (portrait)
■ High-luminance monochrome LCD panel, which has wide view angle, is used for this product. It is designed for medical image display.
■ Luminance stabilization function composed with luminance sensor and luminance control circuit always observes the luminance and makes it stable.
■ Images are faithfully displayed along grayscale characteristics (DICOM GSDF) based on the calibrated data stored to the lookup table of the monitor.
■ It minimizes luminance unevenness by Uniformity Correction Function to achieve the uniformity of luminance on the whole screen.
■ Quantitative evaluation and visual evaluation are done before the shipment. Quality control along the QC guideline is conducted.

The provided text describes the 510(k) premarket notification for the JVCKENWOOD 3MP Monochrome Digital Mammography LCD Monitor MS-S300. It focuses on demonstrating substantial equivalence to a predicate device rather than a comprehensive study proving the device meets specific clinical acceptance criteria.

However, based on the Substantial Equivalence Comparison table and Recommended Physical Laboratory Tests, we can infer some performance aspects and acceptance criteria for the display itself.

1. Table of acceptance criteria and the reported device performance:

Since this is a medical display, the "acceptance criteria" largely revolve around meeting or exceeding the technical specifications of the predicate device and adhering to recognized industry standards for medical displays (like AAPM-TG18 and DICOM GSDF). The "reported device performance" is essentially its technical specifications and how they compare to the predicate.

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

The document describes a technical evaluation of a medical display monitor, not a clinical study on medical images. Therefore, "test set" and "data provenance" in the context of clinical images are not applicable here. The testing involves the physical device itself.

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

This information is not explicitly provided. The tests described are objective physical and photometric measurements conducted on the display device, likely by engineers or technicians using specialized equipment and conforming to established standards (e.g., AAPM-TG18, ISO13406-2). There is no mention of human experts establishing "ground truth" for these technical performance metrics.

4. Adjudication method for the test set:

Not applicable in the sense of clinical image review. The evaluation methods are technical measurements against objective criteria and standards.

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 monitor, not an AI algorithm. Therefore, an MRMC study related to AI assistance would not be relevant.

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

Not applicable. This device is a display monitor, not an algorithm.

7. The type of ground truth used:

For the technical performance of the monitor, the "ground truth" is defined by the objective measurement standards and physical properties specified in documents like AAPM-TG18, ISO13406-2, VESA 2001, and the DICOM GSDF standard. For example, "Luminance Response at 256 digital values by AAPM-TG18" means the measured luminance values at those digital inputs are compared against the expected values defined by the AAPM-TG18 guideline (which aligns with DICOM GSDF).

8. The sample size for the training set:

Not applicable, as this is a physical medical display monitor, not a machine learning model.

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

Not applicable, as this is a physical medical display monitor, not a machine learning model.

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