(53 days)
The CL24N LCD Monitor System is intended to be used in displaying digital medical images for review by trained medical practitioners. It does not support the display of mammography images for diagnosis.
CL24N is intended to display high resolution color and gravscale medical imaging for PACS and Radiology system. This Medical Monitor is intended to be used by trained medical practitioners for displaying and reviewing of medical images.
EzCal is a software solution which enables the user to modify display output to meet DICOM Part 14 GSDF and other key industry standards.
EzCal is packed with the display.
While using the CL24N product, use the EzCal S/W provided as a bundle to periodically check whether the product meets the intended use.
If the product does not meet the intended use, the product must be returned to the manufacturer or an authorized service center to be calibrated to a product that can be used normally.
The CL24N device is a 2.1MP Color LCD Monitor intended for displaying digital medical images for review by trained medical practitioners. It is not intended for displaying mammography images for diagnosis.
Here's a breakdown of the acceptance criteria and the study conducted to prove the device meets these criteria:
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria are implied by the tests performed to demonstrate compliance with "Display Devices for Diagnostic Radiology - Guidance for Industry and Food and Drug Administration Staff, issued on October 2, 2017." The reported device performance is that it met these criteria.
| Test Item | Acceptance Criteria (Implied by standard) | Reported Device Performance |
|---|---|---|
| Pixel Defects | Meets industry standards for acceptable pixel defects in medical displays. | Met acceptance criteria. |
| Artifacts | Absence of artifacts that would interfere with medical image review. | Met acceptance criteria. |
| Luminance | Achieves specified luminance levels for medical image display (e.g., DICOM calibrated luminance, maximum luminance). | Met acceptance criteria. |
| Reflection | Meets industry standards for minimal reflection to ensure clear image viewing. | Met acceptance criteria. |
| Luminance Uniformity | Achieves uniform luminance across the display surface. | Met acceptance criteria. |
| Veiling Glare | Meets industry standards for minimal veiling glare to maintain image contrast. | Met acceptance criteria. |
| Color Uniformity | Achieves uniform color display across the screen. | Met acceptance criteria. |
| Luminance Response | Complies with DICOM Part 14 Grayscale Standard Display Function (GSDF) for accurate grayscale rendition. | Met acceptance criteria. |
| Luminance at 30° and 45° in horizontal, and vertical directions | Maintains acceptable luminance at specified viewing angles. | Met acceptance criteria. |
| Luminance Stability Test | Demonstrates stable luminance over time and operating conditions. | Met acceptance criteria. |
| Color Tracking | Accurate and consistent color reproduction. | Met acceptance criteria. |
| Gray Tracking | Accurate and consistent grayscale reproduction. | Met acceptance criteria. |
| MTF (Modulation Transfer Function) | Meets specified MTF characteristics for image sharpness and detail. | Met acceptance criteria. |
2. Sample Size Used for the Test Set and Data Provenance
The document does not specify a separate "test set" in the context of an AI/algorithm study. The tests mentioned were performed on the CL24N device itself, not on a dataset of medical images. Therefore, the "sample size" refers to the individual device undergoing testing.
- Sample Size for Test Set: Not applicable as it's a device performance test, not a data-driven algorithm test. The testing was conducted on the CL24N device.
- Data Provenance: Not applicable. The testing directly evaluated the monitor's physical and display characteristics.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
Not applicable. The tests performed are objective, physics-based measurements of the monitor's display characteristics (e.g., luminance, uniformity, pixel defects). They do not require expert interpretation to establish ground truth in the way medical images would for diagnostic algorithms.
4. Adjudication Method for the Test Set
Not applicable. The tests are direct measurements against pre-defined technical specifications and industry standards. There is no need for expert adjudication.
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 MRMC comparative effectiveness study was done. This device is a medical monitor, an output display device, and does not incorporate AI for diagnostic assistance.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
No standalone algorithm performance study was done. As stated, this is a display monitor, not a diagnostic algorithm.
7. The Type of Ground Truth Used
The "ground truth" for the device's performance is established by the specified technical parameters and compliance with recognized industry standards (e.g., IEC 60601-1, DICOM Part 14 GSDF, and the FDA's guidance document "Display Devices for Diagnostic Radiology"). The device's characteristics are measured and compared against these objective standards.
8. The Sample Size for the Training Set
Not applicable. This device is a physical monitor, not a machine learning model. There is no training set involved.
9. How the Ground Truth for the Training Set Was Established
Not applicable. As there is no training set.
§ 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).