(243 days)
CX50N LCD Monitor System is intended to be used in displaying and viewing digital medical images for review and analysis by trained medical practitioners. It is specifically designed for digital mammography applications and digital breast tomosynthesis applications.
CX50N LCD Monitor 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.
CX50N is being provided with the calibration software EzCal v.2 (developed by Qubyx Inc.) when requested by the customer.
The provided text is a 510(k) Summary for the WIDE Corporation CX50N LCD Monitor System. It describes the device, its intended use, and a comparison to a predicate device. However, it does not contain the detailed information needed to fill out all the specific sections of your request regarding acceptance criteria and the study proving the device meets them, particularly concerning AI or diagnostic performance studies.
The document focuses on the monitor's performance as a display device, not on an AI algorithm detecting medical conditions. The "acceptance criteria" discussed are related to display characteristics and compliance with electrical safety and electromagnetic compatibility standards, not diagnostic accuracy.
Here's a breakdown of what can and cannot be answered based on the provided text:
What can be extracted:
- 1. A table of acceptance criteria and the reported device performance: This is partially available for the monitor's display characteristics, but not for diagnostic performance.
- 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Based on the text, no such study was performed for an algorithm. The device is a monitor.
- 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable for a display device. The "ground truth" would relate to the display's ability to accurately represent images, not diagnose.
- 8. The sample size for the training set: Not applicable as there is no AI algorithm being trained.
- 9. How the ground truth for the training set was established: Not applicable.
What cannot be extracted from the provided text:
- 2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): The document mentions "bench tests" for the monitor's performance but doesn't detail sample sizes or data provenance in the way one would for a diagnostic AI study.
- 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience): Not applicable for a display device clearance.
- 4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
- 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 monitor, not an AI-powered diagnostic tool.
Based on the provided document (K222719) for the CX50N LCD Monitor System:
The device under review is primarily an LCD Monitor System intended for displaying and viewing digital medical images, specifically for mammography and digital breast tomosynthesis applications. It is not an AI diagnostic algorithm. Therefore, many of the questions related to AI algorithm performance studies, expert ground truth establishment, and MRMC studies are not applicable to this device's clearance information.
The "acceptance criteria" and "study" described in the document relate to the physical and functional performance of the medical display monitor itself, ensuring it meets standards for image display quality and safety.
1. A table of acceptance criteria and the reported device performance
The document provides a comparison of technological characteristics between the subject device (CX50N) and its predicate device (K170783). While not explicitly labeled as "acceptance criteria," these are the performance metrics assessed. The "reported device performance" is essentially that the subject device's specifications are either the same or acceptable improvements/differences compared to the predicate.
| Attribute | Acceptance Criteria (Predicate / Standard) | Reported Device Performance (Subject CX50N) | Discussion / Compliance |
|---|---|---|---|
| Intended Use | Display digital medical images for review and analysis by trained medical practitioners; specifically for digital mammography and digital breast tomosynthesis. | Same | Complies. |
| Response Time (typical) | 25ms (On/Off) | 25ms (On/Off) | Complies. |
| LCD Panel size | 21.3" | 21.3" | Complies. |
| Resolution | 2560 x 2048 | 2560 x 2048 | Complies. |
| Pixel pitch | 0.165 mm x 0.165mm | 0.165 mm x 0.165mm | Complies. |
| Maximum luminance | 1,200 cd/m² | 1,150 cd/m² | Difference noted (lower for subject device), but deemed acceptable and likely within manufacturer's panel specs. |
| Contrast Ratio | 1500 : 1 | 2000 : 1 | Improvement noted (higher for subject device), deemed acceptable. |
| Input signal | DVI-I, DisplayPort | DVI-I, DisplayPort | Complies. |
| Power Supply | 100~240 VAC, 50/60Hz | 100~240 VAC, 50/60Hz | Complies. |
| Color/Monochrome | Color | Color | Complies. |
| QC software | Lumical Advanced | EzCal | Different software, but functions are similar. Deemed acceptable. |
| Firmware | Version: N1220_221229 | Version: N1220_221229 | Complies (No change). |
| Luminance Non-uniformity compensation | Luminance Uniformity Correction | Luminance Uniformity Correction | Complies. |
| Sensors | Backlight Sensor, IQ Sensor, Ambient Light Sensor | Same | Complies. |
| USB Ports / Standard | 1 upstream, 3 downstream / Rev. 3.0 | Same | Complies. |
| Dimensions (w stand) | 390.3 x 520.1 x 248.8 mm | Same | Complies. |
Additional Bench Test Items (from Section 8):
The device was tested against instructions in 'Display Devices for Diagnostic Radiology - Guidance for Industry and Food and Drug Administration Staff, issued on October 2, 2017. Specific test items include:
- Pixel Defects
- Artifacts
- Luminance
- Reflection
- Luminance Uniformity
- Veiling Glare
- Color Uniformity
- Luminance Response
- Luminance at 30° and 45° in horizontal and vertical directions
- Temporal Performance Test
- Color Tracking
- Gray Tracking
- MTF (Modulation Transfer Function)
The document concludes that the "results of these tests demonstrate that CX50N meets the acceptance criteria and is adequate for this intended use."
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
The document states "The bench tests below were performed on the CX50N". It does not specify a "sample size" in the context of cases/patients, as this is a hardware device test. It likely refers to testing a specific number of manufactured units or prototypes of the CX50N monitor. Data provenance (country, retrospective/prospective) is not applicable here as it is about hardware testing, not clinical data collection.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
Not applicable. The "ground truth" for a monitor is its specified technical performance characteristics and compliance with industry standards (e.g., DICOM Part 14 GSDF). These are measured instrumentally, not by human expert consensus on images.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. Adjudication methods are typically used for establishing ground truth in diagnostic studies involving human interpretation, not for validating hardware performance.
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. This device is a display monitor, not an AI diagnostic aid. Therefore, no MRMC study or assessment of human reader improvement with AI assistance was performed or is relevant for this clearance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Yes, in the sense that the monitor's performance was tested independently of human users' diagnostic abilities. However, this is not an "algorithm only" study as no diagnostic algorithm is part of this device cleared in this application. The standalone "performance" refers to the monitor's display characteristics.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
The "ground truth" for this device's performance evaluation refers to established technical specifications and compliance with international and FDA-recognized consensus standards for medical displays (e.g., DICOM Part 14 GSDF, IEC 60601-1, IEC 60601-1-2) which are measured by instruments. It is not expert consensus, pathology, or outcomes data related to disease diagnosis.
8. The sample size for the training set
Not applicable. This device is a monitor and does not involve a training set for an AI algorithm.
9. How the ground truth for the training set was established
Not applicable. This device is a monitor and does not involve a training set for an AI algorithm.
§ 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).