(55 days)
This Product is indicated for use in displaying radiological images for review, analysis, and diagnosis by trained medical practitioners. The display is not intended for mammography.
RadiForce RX670 is a color LCD monitor for viewing medical images other than those of mammography. The color panel employs in-plane switching (IPS) technology allowing wide viewing angles and the matrix size (or resolution) is 3,280 x 2,048 pixels (6MP) with a pixel pitch of 0.1986 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. This helps ensure tone curves even if a display controller or workstation must be replaced or serviced.
There are two model variations. RX670 and RX670-AR. The difference of the two variations is the surface treatment of the display screens; the surface treatment of the RX670 is Anti-Glare (AG) treatment and that of the RX670-AR is Anti-Reflection (AR) coating.
RadiCS is application software to be installed in each workstation offering worry-free quality control of diagnostic monitors including the RadiForce RX670 based on the QC standards and guidelines and is capable of quantitative tests and visual tests defined by them. The RadiCS is included in this 510(k) submission as an accessory to the RadiForce RX670.
RadiCS is Basic Documentation level and that it's being used unchanged from the predicate software. RadiCS supports the functions of the monitor RadiForce RX670 and it's not a medical imaging software.
The provided document, K241441, is a 510(k) premarket notification for the RadiForce RX670 and RadiForce RX670-AR medical display monitors. The document demonstrates substantial equivalence to predicate devices (RadiForce RX660, RX660-AR, and RadiForce RS340) for displaying radiological images for review, analysis, and diagnosis, excluding mammography.
Here's an analysis of the acceptance criteria and study information based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance:
The document refers to "pre-defined criteria" but does not explicitly list the numerical acceptance criteria for each test. Instead, it states that the device "meet[s] the pre-defined criteria when criteria are set" and that its display characteristics are "equivalent to those of the predicate device."
| Acceptance Criteria | Reported Device Performance (RadiForce RX670) |
|---|---|
| Spatial Resolution (MTF) | Demonstrated display characteristics "equivalent to those of the predicate device." |
| Pixel Defects/Faults (Maximum allowed number) | Demonstrated display characteristics "equivalent to those of the predicate device." |
| Miscellaneous Artifacts (Visual check based on TG18 guideline) | Demonstrated display characteristics "equivalent to those of the predicate device." |
| Temporal Response | Demonstrated display characteristics "equivalent to those of the predicate device." |
| Luminance | Demonstrated display characteristics "equivalent to those of the predicate device." |
| Conformance to DICOM GSDF (as per AAPM TG18 guideline) | Demonstrated display characteristics "equivalent to those of the predicate device." |
| Color Tracking | Demonstrated display characteristics "equivalent to those of the predicate device." |
2. Sample size used for the test set and the data provenance:
- Sample Size: The document does not explicitly state the number of RadiForce RX670 units tested. It refers to "the RadiForce RX670" (singular), implying that one or more units of the product were subjected to the specified bench tests.
- Data Provenance: The testing was "bench tests," conducted by the manufacturer, EIZO Corporation, in Japan. The data is thus prospective, originating from the manufacturer's internal testing.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
This information is not applicable as the study involved bench tests of a medical display device, not a study evaluating human interpretation of images or an AI algorithm's diagnostic performance against a ground truth.
4. Adjudication method for the test set:
This information is not applicable as the study involved bench tests of a medical display device, not a study requiring adjudication of expert opinions or diagnostic outcomes.
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, an MRMC comparative effectiveness study was not done. This document is for a medical display device, not an AI-powered diagnostic algorithm.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:
No, a standalone algorithm performance study was not done. This document is for a medical display device, which is hardware, and its associated quality control software (RadiCS). It does not involve a diagnostic algorithm.
7. The type of ground truth used:
This information is not applicable in the traditional sense of diagnostic accuracy studies. For the bench tests, the "ground truth" would be the engineering specifications and established standards (e.g., DICOM GSDF, AAPM TG18 guideline) against which the display's performance characteristics were measured.
8. The sample size for the training set:
This information is not applicable. The device is a display monitor, not an AI algorithm that requires a training set. The RadiCS software mentioned is for quality control and "is not a medical imaging software" and "it's being used unchanged from the predicate software," implying no new algorithm development or training.
9. How the ground truth for the training set was established:
This information is not applicable for the reasons stated in point 8.
§ 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).