(85 days)
21.3-inch (54cm) Color LCD Monitor CCL212 (CDL2107A) is to be used in conjunction with the picture archiving communication systems (PACS) for medical imaging applications. It is not meant to be used for digital mammography.
21.3-inch (54cm) high definition color medical imaging LCD monitor with higher luminance (450cd/m²) and longer lifetime (three times as long as our existing models). With circuit, mounted, CCL212 (CDL2107A) delivers stable display of images.
This is a 510(k) summary for a medical display monitor (CCL212/CDL2107A), not an AI/ML device. Therefore, the details requested about acceptance criteria, study design, expert involvement, and ground truth for an AI device are not applicable.
The document describes the device, its intended use, and claims substantial equivalence to a predicate device based on improved display characteristics. It does not contain information about studies involving patient outcomes, diagnostic accuracy, or human reader performance, as would be relevant for an AI diagnostic aid.
Here's a breakdown of what can be extracted or inferred from the provided text, and where the requested AI-specific information is missing:
1. A table of acceptance criteria and the reported device performance
- Acceptance Criteria (Implicit for a medical display):
- Display area: Superior to predicate device CDL2005A (K021738)
- Luminance: Higher (450 cd/m²) than predicate device CDL2005A (K021738)
- Lifetime: Three times longer than existing models (and by inference, the predicate)
- Stability: Delivers stable display of images (due to mounted circuit)
- Intended Use Compliance: Suitable for medical imaging applications with PACS (excluding digital mammography).
- Reported Device Performance:
- Display Area: Superior to predicate
- Luminance: 450 cd/m² (higher than predicate)
- Lifetime: Three times longer than "existing models"
- Stability: Achieved via circuit mounting.
- Use Case: As intended with PACS for general medical imaging.
2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)
- Not Applicable: This is a hardware device (monitor). There is no "test set" of medical images or data in the context of an AI/ML model for which this information would be relevant. The evaluation would have been on the physical and electronic characteristics of the monitor itself.
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: No "ground truth" or expert review of medical images on the device is described, as it's a display hardware. Product testing would involve engineers and quality control personnel.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Not Applicable: No adjudication of medical image interpretations took place.
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 filing describes a display monitor, not an AI diagnostic tool. No MRMC study was performed or is relevant for this device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Not Applicable: As above, this is a hardware device, not an algorithm.
7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)
- Not Applicable: No medical "ground truth" was established or used in this filing for the monitor. The "ground truth" for the monitor itself would be its physical specifications and performance against engineering standards.
8. The sample size for the training set
- Not Applicable: This is not an AI/ML device, so there is no training set.
9. How the ground truth for the training set was established
- Not Applicable: This is not an AI/ML device, so there is no training set or associated ground truth establishment.
In summary, the provided text describes a predicate review for a medical display monitor, focusing on its technical specifications and substantial equivalence to an existing device. It does not provide the types of information relevant to the testing and validation of an artificial intelligence or machine learning medical 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).