(54 days)
The CX30N(CX30PQX) LCD Monitor System is intended to be used in displaying and viewing digital medical images for review and analysis by trained medical practitioners. The display is not intended for mammography.
CX30N(CX30PQX) is intended to provide high resolution color and grayscale medical imaging for PACS and Radiology system. This Medical Monitor is intended to be used by trained medical practitioners for displaying, reviewing, and analysis of medical images.
EzCal ver.2 is a software solution which enables the user to modify display output to meet DICOM Part 14 GSDF and other key industry standards.
CX30N(CX30PQX) is being provided with the calibration software EzCal v.2 (developed by Qubyx Inc.) when requested by the customer.
CX30N is basic model and CX30PQX is identical to CX30N, except model name.
The provided text is a 510(k) summary for the CX30N (CX30PQX) medical display device. It describes the device's technical specifications, intended use, and a comparison with a predicate device to establish substantial equivalence. The document describes bench tests for performance, but it does not describe a study involving human readers, AI assistance, or the establishment of ground truth for diagnostic accuracy.
Therefore, many of the requested items (2-9) in the prompt cannot be answered from the provided text, as they pertain to clinical or standalone performance studies, which were not conducted or reported for this device based on the provided summary. The device in question is a medical monitor (hardware), not an AI algorithm or a diagnostic software.
Here's what can be extracted from the provided text regarding acceptance criteria and performance, as well as the limitations:
1. A table of acceptance criteria and the reported device performance
The document lists various "Test Items" that were performed, implying these are the parameters for which acceptance criteria would have been defined. However, the specific numerical acceptance criteria and the reported numerical performance values are not explicitly stated in the provided 510(k) summary. It only indicates that "CX30N(CX30PQX) meets the acceptance criteria."
| Test Item | Acceptance Criteria (Not explicitly stated in document) | Reported Device Performance (Not explicitly stated in document) |
|---|---|---|
| Pixel Defects | To meet specified standards | Meets acceptance criteria |
| Artifacts | To meet specified standards | Meets acceptance criteria |
| Luminance | To meet specified standards | Meets acceptance criteria |
| Reflection | To meet specified standards | Meets acceptance criteria |
| Luminance Uniformity | To meet specified standards | Meets acceptance criteria |
| Resolution | To meet specified standards | Meets acceptance criteria |
| Noise | To meet specified standards | Meets acceptance criteria |
| Veiling Glare | To meet specified standards | Meets acceptance criteria |
| Color Uniformity | To meet specified standards | Meets acceptance criteria |
| Luminance Response | To meet specified standards | Meets acceptance criteria |
| Luminance at 30° and 45° in diagonal, horizontal, and vertical directions | To meet specified standards | Meets acceptance criteria |
| Temporal Performance test | To meet specified standards | Meets acceptance criteria |
| Color Tracking | To meet specified standards | Meets acceptance criteria |
| Gray Tracking | To meet specified standards | Meets acceptance criteria |
Note: The phrase "meets the acceptance criteria" is a general statement. For a detailed understanding, the actual criteria (e.g., maximum allowable pixel defects, specific luminance range, etc.) and the measured values would be needed, which are not in this summary. The tests were performed "following the instructions in 'Display Devices for Diagnostic Radiology - Guidance for Industry and Food and Drug Administration Staff, issued on October 2, 2017.'" This guidance would contain the specific acceptance criteria.
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
This information is not applicable as the described tests are bench tests of a physical display device, not clinical or image-based studies with patient data.
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 for the reasons stated above. Ground truth, in this context, would relate to image interpretation, not display performance.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This information is not applicable for the reasons stated above.
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
An MRMC study was not performed as this device is a medical monitor, not an AI-powered diagnostic tool. The document explicitly states "No clinical studies were considered necessary and performed."
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
A standalone performance study was not performed beyond the physical bench tests for the display's technical specifications. This device is not an algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
This information is not applicable for the reasons stated above. For the bench tests, the "ground truth" would be the engineering specifications and calibrated measurement tools for display performance.
8. The sample size for the training set
This information is not applicable. This document describes a medical display monitor, not a machine learning model.
9. How the ground truth for the training set was established
This information is not applicable. This document describes a medical display monitor, not a machine learning model.
In summary, the provided document focuses on the technical specifications and bench testing of a medical display monitor to prove its substantial equivalence to a predicate device. It explicitly states that "No clinical studies were considered necessary and performed," indicating that the device approval did not hinge on human reader studies or AI performance metrics.
§ 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).