(21 days)
The Medical LCD Monitor is intended to be used in displaying and viewing medical digital images for review and analysis by trained medical practitioners. It is specifically designed for digital mammography applications.
The Medical LCD Monitor (Model: KT-D213V5E) is designed for the purpose of medical applications such as X-ray, radiology, MRI, endoscopy or mammography imaging display. The high-resolution LCD panel with a resolution of 2560 x 2048, combined with a high performance image processing controller, provides the users high-definition and high-quality medical image displays. The medical monitor complies with international EMC/ safety standards.
The provided document is a 510(k) premarket notification for a Medical LCD Monitor (Model: KT-D213V5E) and primarily focuses on demonstrating substantial equivalence to a predicate device. It does not contain information about an AI/ML-driven medical device, nor does it detail a study that proves the device meets specific acceptance criteria in the context of an AI/ML model's performance.
Therefore, I cannot provide the requested information regarding acceptance criteria, study details, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, or ground truth for an AI/ML device based on this document. The document describes a medical display monitor and its technical specifications, along with standard performance tests to ensure basic functionality and safety/effectiveness consistent with its predicate.
The "Performance Data" section lists several tests performed on the subject device and states that "passed the pre-set criteria," but it does not specify what those criteria are or provide any detailed results.
Here's a summary of what is available in the document, which might be similar to how a non-AI medical device would list performance:
1. A table of acceptance criteria and the reported device performance
The document lists performance tests that were conducted and states they "passed the pre-set criteria," but it does not provide the specific acceptance criteria or detailed reported performance data for these tests.
| Performance Test | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Luminance Response | Not Specified | Passed pre-set criteria |
| Uniformity | Not Specified | Passed pre-set criteria |
| Miscellaneous Test | Not Specified | Passed pre-set criteria |
| Display Reflection | Not Specified | Passed pre-set criteria |
| Angular dependencies | Not Specified | Passed pre-set criteria |
| Clinical Reference Image | Not Specified | Passed pre-set criteria |
| Geometric Distortion | Not Specified | Passed pre-set criteria |
| Display Noise | Not Specified | Passed pre-set criteria |
| Display Veiling Glare | Not Specified | Passed pre-set criteria |
| Scheduler Table | Not Specified | Passed pre-set criteria |
| EMC and Electrical Safety Tests | Not Specified | Passed pre-set criteria |
| Software Verification and Validation | Not Specified | Passed pre-set criteria |
Since the device described is a Medical LCD Monitor and not an AI/ML algorithm, the following points are not applicable and cannot be answered based on the provided text:
- 2. Sample size used for the test set and the data provenance (Not applicable for a display monitor's technical performance tests)
- 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (Not applicable, as "ground truth" in the context of expert diagnosis from images is not relevant for a display monitor's technical performance)
- 4. Adjudication method 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, as this is not an AI device)
- 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done (Not applicable, as this is not an AI algorithm)
- 7. The type of ground truth used (Not applicable)
- 8. The sample size for the training set (Not applicable, as there is no training set for a display monitor)
- 9. How the ground truth for the training set was established (Not applicable)
§ 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).