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The 6MP Color LCD Monitor (C61W+) is intended to be used in displaying digital images for review and analysis by trained medical practitioners. It does not support the display of mammography images for diagnosis.

C61W+ is a 30-inch TFT color LCD monitor. It is specifically designed to display high quality, high definition images required for general radiography. With built-in multi-display modes, the monitor facilitates convenient viewing by doctors of various images, data, and layout of information. The product is designed to be able to meet DICOM 3.14 calibration standards. With features such as brightness stability control, 14 bit image processing technology, and interfaces with external calibration software, the monitor ensures optimal viewing conditions over the life of the display. The specially designed base supports height and tilt adjustments for ergonomic comfort.

The provided text describes the 510(k) summary for the Shenzhen Beacon Display Technology Co., Ltd.'s 6MP Color LCD Monitor (C61W+). It outlines the device's technical specifications and compares it to a predicate device, the EIZO Corporation's 6MP Color LCD Monitor, RadiForce RX650 (K134002).

However, this document does not contain information related to an AI/ML-based medical device study. The device in question is a medical display monitor, and the "acceptance criteria" and "study" described are focused on verifying its display performance characteristics (e.g., DICOM conformance, luminance, chromaticity, pixel defects) against established standards (AAPM TG18 guideline) and demonstrating substantial equivalence to a predicate device.

Therefore, I cannot extract the detailed information requested regarding acceptance criteria and studies that prove the device meets those criteria for an AI/ML product, as the provided text is about a display monitor and not an AI/ML medical device.

The document does mention performance tests done on the monitor to show its equivalence to the predicate device. Here's what can be extracted from the provided text about the monitor's performance testing:

Device: 6MP Color LCD Monitor (C61W+)

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

The document doesn't present precise quantitative "acceptance criteria" in a table format for each test, but it states that "The test results showed that C61W+ is with display characteristics equivalent to those of the predicate device, RadiForce RX650 except some items, each of which was determined that it would not affect observer's performance."

Here's a summary of the performance tests mentioned, which implicitly define the "acceptance criteria" as conforming to the specified standards or demonstrating equivalence to the predicate:

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

• Sample Size: Not specified. The summary implies testing was done on the C61W+ monitor itself, likely one or a few units, rather than a "test set" of images or patient data.
• Data Provenance: Not applicable as it's a display monitor, not an AI/ML algorithm processing data. The tests are physical measurements and visual inspections of the monitor.

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 an AI/ML device, refers to disease presence/absence or other clinical labels. For a display monitor, the "ground truth" is adherence to technical specifications and established display standards (e.g., DICOM GSDF). The "experts" would be the engineers and technicians performing the measurements and visual checks, trained in display calibration and quality control. Their number and specific qualifications are not detailed in this summary.

4. Adjudication method for the test set:

• Not applicable. This concept is relevant for human-in-the-loop or expert consensus for AI/ML algorithm evaluation, not for physical device testing of a monitor.

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:

• No. This study type is for evaluating the impact of an AI algorithm on human reader performance, which is not relevant for a medical display monitor.

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

• Not applicable. This is for AI/ML algorithms, not for a display monitor. The performance tests mentioned (DICOM conformance, luminance, etc.) are "standalone" in the sense they evaluate the device itself.

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

• The "ground truth" for a medical display monitor is its adherence to industry standards for display quality, such as:
  • AAPM TG18 guideline: For display performance assessment (DICOM GSDF conformance, luminance, chromaticity non-uniformity, artifacts).
  • FDA Staff Guidance: Display Accessories for Full-Field Digital Mammography Systems: For specific chromaticity measurements.
  • Internal specifications for pixel defects/faults.

8. The sample size for the training set:

• Not applicable. There is no AI/ML algorithm being trained for this device.

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

• Not applicable. There is no AI/ML algorithm being trained for this device.

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