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The RadiForce RX340 is intended to be used in displaying and viewing digital images for diagnosis of X-ray or MRI, etc. by trained medical practitioners. The device does not support the display of mammography images for diagnosis.

The RadiForce RX340 is a color LCD monitor for viewing medical images other than those of mammography. The matrix size (or resolution) of the panel is 1,536 x 2,048 pixels (3MP) with a pixel pitch of 0.21075 mm. Since factory calibrated display modes, each of which is characterized by a specific tone curve (including DICOM GSDF), a specific luminance range and a specific color temperature, are stored in lookup tables within the monitor, the tone curve is e.g. DICOM compliant regardless of the display controller used. RadiCS is application software to be installed in each workstation offering worry-free quality control of the diagnostic monitors including RX340 based on several QC guidelines. The RadiCS and its subset, RadiCS LE are included in this 510(k) submission as an accessory to the RadiForce RX340.

The provided document is a 510(k) summary for the EIZO RadiForce RX340 3MP Color LCD Monitor. It does not contain information about a study proving the device meets specific acceptance criteria in the way a clinical trial for an AI-powered diagnostic device would.

This document describes a medical display monitor and its substantial equivalence to a predicate device, not an algorithm that generates a diagnosis or interpretation. Therefore, many of the requested items (e.g., sample size for test set, number of experts for ground truth, adjudication method, MRMC study, standalone performance, training set size) are not applicable to this type of device submission.

Instead, the submission focuses on technical characteristics and performance benchmarks compared to a predicate device to demonstrate substantial equivalence.

Here's an analysis based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't present a formal table of "acceptance criteria" against which an algorithm's performance is measured. Instead, it compares the proposed device's technical specifications and performance to those of a predicate device (RadiForce RX320). The "acceptance criteria" are implicitly that the proposed device must perform at least as well as the predicate device in key display characteristics, or that any differences do not affect safety or effectiveness.

2. Sample Size for the Test Set and Data Provenance

Not applicable in the context of an algorithm's test set. The "test set" here refers to the actual monitor units evaluated. The document states: "EIZO NANAO CORPORATION performed a range of system and performance tests to ensure that the RadiForce RX340 performs in accordance with its specifications." It does not specify the number of units tested, but it is typical for manufacturers to test a representative sample during design validation. Data provenance is internal testing by the manufacturer.

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

Not applicable. "Ground truth" in this context would relate to the physical characteristics of the display itself (e.g., luminance, resolution, color accuracy), which are measured using calibrated instruments, not expert human assessment.

4. Adjudication Method for the Test Set

Not applicable. Measurements are objective and do not require expert adjudication.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No. This type of study is relevant for comparing the diagnostic performance of human readers with and without AI assistance for interpreting medical images. The RadiForce RX340 is a display monitor, not an AI diagnostic tool, so an MRMC study is not relevant.

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

Not applicable. The device is a monitor, not an algorithm.

7. The Type of Ground Truth Used

The ground truth used for validating the monitor's performance is based on objective physical measurements of its display characteristics (e.g., luminance, color temperature, resolution, DICOM GSDF compliance) using calibrated testing equipment. It also includes compliance with international safety and EMC standards.

8. The Sample Size for the Training Set

Not applicable. This is a hardware device (monitor), not a machine learning algorithm that requires a training set.

9. How the Ground Truth for the Training Set Was Established

Not applicable, as there is no training set for a monitor.

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