(57 days)
This product is intended to be used in displaying digital images, including standard and multiframe digital mammography, for review, analysis and diagnosis by trained medical practitioners. It is specially designed for breast tomosynthesis applications.
RadiForce RX560 is a color LCD monitor for viewing medical images including those of mammography. The color panel employs in-plane switching (IPS) technology allowing wide viewing angles and the matrix size (or resolution) is 2,048 x 2,560 pixels (5MP) with a pixel pitch of 0.165 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.
There are two model variations, RX560 and RX560-AR . The difference of the two variations is the surface treatment of the display screens; the surface treatment of the RX560 is Anti-Glare (AG) treatment and that of the RX560-AR is Anti-Reflection (AR) coating.
Two RX560 monitors mounted on a single stand configuration is available identified by with "MD" like RX560-MD and RX560-AR-MD.
RadiCS is application software to be installed in each workstation offering worry-free quality control of the diagnostic monitors including the RadiForce RX560 based on the QC standards and guidelines and is capable of quantitative tests and visual tests defined by them. The RadiCS and its subset, RadiCS LE, are included in this 510(k) submission as an accessory to the RadiForce RX560.
The provided text describes a 510(k) summary for the EIZO RadiForce RX560, a color LCD monitor for medical imaging, including mammography and breast tomosynthesis. The document focuses on demonstrating substantial equivalence to a predicate device (RadiForce GX550) rather than presenting a standalone study with acceptance criteria for an AI-powered diagnostic device.
Therefore, many of the requested elements for an AI device's acceptance criteria and study are not applicable or cannot be extracted from this document, as it concerns a display monitor, not an AI algorithm.
However, I can extract the performance testing performed and the general findings related to the display characteristics.
Here's the breakdown of what can be inferred and what cannot, based on the provided text:
1. A table of acceptance criteria and the reported device performance
The document defines "pre-defined criteria" for the display characteristics, but the specific numerical values of these criteria are not explicitly stated. The reported performance is that the device "meet[s] the pre-defined criteria when criteria are set."
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Conformance to DICOM GSDF (TG18 guideline) | Meets criteria |
| Angular dependency of luminance response (horizontal, vertical, diagonal) | Meets criteria |
| Luminance non-uniformity (TG18 guideline) | Meets criteria |
| Chromaticity non-uniformity (TG18 guideline) | Meets criteria |
| Chromaticity at center (5%, 50%, 95% max luminance) | Meets criteria |
| Display reflections (specular, diffuse, haze) | Meets criteria |
| Small-spot contrast ratio | Meets criteria |
| Spatial resolution (MTF) | Meets criteria |
| Noise (NPS) | Meets criteria |
| Pixel aperture ratio | Meets criteria |
| Absence of miscellaneous artifacts (TG18 guideline) | Meets criteria |
| Temporal response | Meets criteria |
| Luminance stability | Meets criteria |
| Maximum allowed pixel defects/faults | Meets criteria |
| Overall display characteristics | Equivalent to predicate device (RadiForce GX550) |
2. Sample size used for the test set and the data provenance
As this is a display monitor and the tests are "bench tests" on the device itself, there isn't a "test set" of medical images in the same way an AI algorithm would have. The 'sample size' would relate to the number of monitors tested, which is not specified but implied to be sufficient for a product release. Data provenance is not applicable.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable. The ground truth for display performance is based on established technical standards and guidelines (e.g., AAPM TG18), not expert interpretation of medical images.
4. Adjudication method for the test set
Not applicable. Testing is objective measurement against technical 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 is a display device, not an AI algorithm.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. This is a display device. The "bench tests" are a form of standalone performance evaluation for the monitor itself.
7. The type of ground truth used
The ground truth is based on established technical specifications and performance guidelines for medical displays, specifically those outlined in:
- AAPM Task Group 18 (TG18 guideline) for display performance assessment.
- "Guidance for Industry and FDA Staff: Display Accessories for Full-Field Digital Mammography Systems-Premarket Notification (510(k)) Submissions."
8. The sample size for the training set
Not applicable. This is a display device, not an AI algorithm. There is no concept of a "training set" in this context.
9. How the ground truth for the training set was established
Not applicable.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).