(29 days)
JUSHA-M260G/JUSHA-M260/M260G/M260 LCD Monitor is intended to be used in displaying and viewing digital images for diagnosis of X-ray or MRI, etc. by trained medical practitioners. The device does not support the display of mammography images for diagnosis.
JUSHA-M260G/JUSHA-M260/M260G/M260 LCD Monitor is the display system with the high resolution (1600*1200), high luminance (800 cd/m²), and 14-bit grayscale (16384 level), built-in DICOM standard LUT. In particular, JUSHA-M260G has real-time DICOM automatic calibration system, with these this display can automatic adjustment according to different requirements in order to achieve the best results.
The product is consisted of the following components:
- 21.3" Mono-TFT LCD Panel
- DMX0302CR0/main board/V1.2
- JUSHA-M260G LCD Monitor software
- Power Adapter
- Data Cable.
The provided text describes a 510(k) submission for the JUSHA-M260G/JUSHA-M260/M260G/M260 LCD Monitor. This device is a diagnostic display, not an AI/ML algorithm. Therefore, the questions related to AI/ML specific acceptance criteria, such as "effect size of human readers improve with AI vs without AI assistance," "standalone performance," "ground truth establishment for training set," and "adjudication method for the test set" are not applicable.
The acceptance criteria for this type of device are based on demonstrating substantial equivalence to a legally marketed predicate device, primarily through non-clinical bench testing for display performance, electrical safety, and electromagnetic compatibility.
Here's a breakdown based on the provided information:
1. A table of acceptance criteria and the reported device performance
Since this is a display monitor, the "acceptance criteria" are generally that the proposed device performs at least as well as, or is substantially equivalent to, the predicate device in key display parameters and safety. Performance is demonstrated through bench testing against established industry standards.
| Acceptance Criteria (Bench Test Demonstrations) | Reported Device Performance / Compliance |
|---|---|
| Display Performance: | |
| Angular dependency of luminance response | Meets performance standards |
| Luminance non-uniformity characteristics | Meets performance standards (TG18 guideline) |
| Chromaticity non-uniformity characteristics | Meets performance standards (TG18 guideline) |
| Small-spot contrast ratio | Meets performance standards |
| Temporal response | Meets performance standards |
| Luminance stability | Meets performance standards |
| Electrical Safety: | Complies with IEC 60601-1 |
| Electromagnetic Compatibility (EMC): | Complies with IEC 60601-1-2 |
| DICOM Standard LUT: | 14-bit: 16384 (Predicate was 12-bit: 4096, demonstrating improvement) |
| Resolution: | 1600 x 1200 / 1200 x 1600 (Same as predicate) |
| Contrast Ratio: | 1400:1 (Same as predicate) |
| Screen Technology: | 21.3" Mono-TFT LCD Panel (Same as predicate) |
| Viewing Angle: | Horizontal 178°, Vertical 178° (Predicate was 176°, slightly improved) |
2. Sample size used for the test set and the data provenance
- Sample Size for Test Set: Not applicable in the context of an AI/ML test set. For a monitor, performance is tested on the device itself against specifications and standards. This is typically done on a single or small number of manufactured units to demonstrate general compliance.
- Data Provenance: Not applicable. The "data" here refers to the physical performance characteristics of the monitor, not a dataset of patient images.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Not applicable. This device is a monitor, not an AI/ML algorithm that generates diagnostic outputs requiring expert ground truth for interpretation. Its performance is measured objectively against technical specifications and established standards (e.g., TG18 guideline).
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
- Not applicable. Adjudication methods are used to establish a consensus ground truth in studies involving human interpretation or AI outputs, which is not relevant for a display monitor's performance evaluation.
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. No MRMC study was conducted or required, as stated in the document ("The subject of this premarket submission, LCD Monitor, did not require clinical studies to support substantial equivalence.").
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
- Not applicable. This device is a display monitor, not an algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
- Not applicable. The "ground truth" for a display monitor is its adherence to universally accepted technical standards for image display (e.g., DICOM, luminance, resolution, contrast). These are measurable objective properties, not clinical "truths."
8. The sample size for the training set
- Not applicable. This is a hardware device (LCD monitor), not a machine learning model. There is no concept of a "training set" for its development.
9. How the ground truth for the training set was established
- Not applicable, as there is no training set for this type of device.
§ 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).