(29 days)
RadiForce RX211 is intended to be used in displaying and viewing digital images for diagnosis of X-ray or MRI, etc. by trained medical practitioners. RadiForce RX211 does not support the display of mammography images for diagnosis.
RadiForce RX211 is a 54cm (21.3") Color LCD display for medical image viewing. RX211 displays high-definition medical imaging.
The provided document is a 510(k) summary for a medical display monitor (Color LCD Monitor, RadiForce RX211), not an AI/ML device. Therefore, much of the requested information regarding AI/ML device performance, ground truth, and study design (e.g., MRMC studies, standalone performance, training sets) is not applicable or present in this document.
The document focuses on demonstrating substantial equivalence to a predicate device (RadiForce RX210) by comparing technological characteristics, primarily brightness and contrast.
Here's an attempt to answer the applicable questions based on the provided text, with clarifications where the information is not relevant to this type of device:
1. A table of acceptance criteria and the reported device performance
The document does not explicitly state "acceptance criteria" in the traditional sense of a performance study for an AI algorithm. Instead, it compares the technological characteristics of the new device (RX211) to its predicate device (RX210) to demonstrate substantial equivalence. The "performance" here refers to specific technical specifications.
| Characteristic | Acceptance Criteria (Predicate Device RX210) | Reported Device Performance (RX211) |
|---|---|---|
| Brightness | 600 cd/m2 | 750 cd/m2 |
| Contrast | (Improved from RX210) | (Improved from RX210) |
| Maximum Resolution | Same as RX210 | Same as RX210 (implied) |
| Software | (Predicate's software) | Modified |
Note: The document states "The brightness improved in 750 cd/m2 from 600 cd/m2. The contrast improved by it." indicating an improvement over the predicate, which serves as the "acceptance" benchmark for equivalence in this context.
2. Sample size used for the test set and the data provenance
The concept of a "test set" in the context of an AI/ML study is not applicable here. This is a hardware device review. There are no images or clinical data used as a "test set" for performance evaluation in this 510(k) submission.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable. Ground truth and expert consensus are irrelevant for a medical display monitor's technical specifications.
4. Adjudication method for the test set
Not applicable. There is no test set or adjudication performed in this context.
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 medical display monitor, not an AI-assisted diagnostic device. Therefore, no MRMC study looking at human reader improvement with AI was conducted or is relevant.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. This is a hardware device, not an algorithm.
7. The type of ground truth used
Not applicable. Ground truth is not a concept used in the regulatory review of a medical display monitor's technical specifications.
8. The sample size for the training set
Not applicable. There is no training set for a medical display monitor.
9. How the ground truth for the training set was established
Not applicable. There is no training set or ground truth for a medical display monitor.
§ 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).