(107 days)
This Medical Monitor is indicated for use in displaying radiological images (including full-field digital mammography and digital breast tomosynthesis) for review, and diagnosis by trained medical practitioners.
The Medical 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
The provided text describes the 510(k) submission for the LG Electronics 32HQ713D Medical Monitor, a device indicated for displaying radiological images. It's important to note that this document does not contain a study proving the device meets acceptance criteria in the way a clinical or AI performance study would. Instead, it demonstrates substantial equivalence to predicate devices through technical comparisons and non-clinical performance testing.
The acceptance criteria are implicitly defined by the functional characteristics and performance parameters expected of a medical monitor for radiological images, particularly for mammography and digital breast tomosynthesis. The performance is demonstrated by comparing these characteristics to established predicate devices and by confirming adherence to relevant standards and guidance documents.
Here's the information extracted and organized as requested:
1. Table of Acceptance Criteria and Reported Device Performance
The "acceptance criteria" here are framed as demonstrating equivalence to predicate devices and compliance with relevant standards and guidance. The reported performance is the claim of equivalence based on the measurements.
| Acceptance Criteria (Measured Performance Aspect) | Reported Device Performance (vs. Predicate Device) |
|---|---|
| a. Spatial Resolution | Equivalent to predicate device (TG18 test pattern, MTF measurements). |
| b. Pixel Defects | Equivalent to predicate device (measurements of pixel defects). |
| c. Artifacts (Ghosting/Image Sticking) | Equivalent to predicate device (5x5 mosaic pattern, 64Gray / 127Gray judgment). |
| d. Temporal Response (Rise/Fall Time) | Equivalent to predicate device (5-95% and 40-60% luminance transitions). |
| e. Luminance (Max/Min/Achievable/Recommended) | Equivalent to predicate device (analysis of measured luminance values). |
| f. Conformance to Grayscale-to-Luminance Function | Equivalent to predicate device (mapping between image values and luminance output for 256+ levels). |
| g. Luminance at 30° and 45° (Off-normal viewing) | Equivalent to predicate device (luminance response for off-normal viewing). |
| h. Luminance Uniformity or Mura Test | Equivalent to predicate device (measurements of luminance uniformity across display). |
| i. Stability of Luminance and Chromaticity with Temperature and Time | Equivalent to predicate device (no display off, consistent display quality without temperature impact). |
| j. Spatial Noise | Equivalent to predicate device (noise levels for TG18 test pattern and recommended luminance values). |
| k. Reflection Coefficient | Equivalent to predicate device (measurements of Rd). |
| l. Veiling Glare or Small-Spot Contrast | Equivalent to predicate device (contrast obtained for TG18 test pattern and luminance values). |
| m. Color Tracking | Equivalent to predicate device (sRGB xy coverage measurements). |
| n. Gray Tracking | Equivalent to predicate device (sRGB UV coverage measurements). |
| Electrical Safety & Essential Performance | Complies with IEC 60601-1:2015+A1:2012+A2:2020. |
| Electromagnetic Compatibility | Complies with IEC 60601-1-2:2014. |
| Usability | Complies with IEC 60601-1-6:2010+A1:2013+A2:2020. |
| Software Validation | Designed, verified, and validated according to a software development process (MODERATE level of concern software). |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: Not applicable in the context of an "AI test set." This submission describes the testing of a medical monitor (hardware/software), not an AI algorithm. The tests conducted (e.g., spatial resolution, luminance) are on the physical device itself. The document does not specify the number of units tested, but this would typically be a small, representative sample of manufactured devices.
- Data Provenance: Not applicable as there is no patient data involved in these non-clinical performance tests of a display monitor. The tests are based on standard test patterns and measurement methodologies.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of those Experts
Not applicable. Ground truth in the context of a medical display's performance relates to its physical characteristics and adherence to technical specifications and industry standards (e.g., DICOM Part 14 for Grayscale Standard Display Function). These are objectively measurable and do not require expert human interpretation to establish a "ground truth" in the way an AI diagnostic model would.
4. Adjudication Method for the Test Set
Not applicable. See point 3.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and if so, what was the effect size of how much human readers improve with AI vs without AI assistance
No MRMC study was performed or described. This submission is for a medical monitor, not an AI-based diagnostic aid that assists human readers.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done
Not applicable. As noted, this is a medical display, not an AI algorithm. Its "performance" is its ability to accurately and consistently render images, which is tested directly.
7. The Type of Ground Truth Used
The "ground truth" for this device's performance is objective technical specifications and industry standards for medical displays. These include:
- Physical measurements (e.g., luminance, resolution, pixel defects).
- Compliance with electrical safety, EMC, and usability standards (e.g., IEC 60601 series).
- Conformance to display functions like the Greyscale Standard Display Function (implicitly referenced through the "conformance to a grayscale-to-luminance function" test).
- The performance of legally marketed predicate devices, which serve as a benchmark for "substantial equivalence."
8. The Sample Size for the Training Set
Not applicable. This device is a medical monitor, not an AI system that requires a "training set" of data.
9. How the Ground Truth for the Training Set Was Established
Not applicable. See point 8.
§ 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).