(14 days)
The Nio 3MP LED (MDNG-3220) Medical Flat Panel Display System is intended to be used as a tool in displaying and viewing digital images (excluding digital mammography) for review and analysis by trained medical practitioners.
The Nio 3MP LED (MDNG-3220) is a high-resolution flat panel LCD display system for reviewing medical images. It consists of an LCD display (MDNG-3220), an optional high-resolution display controller board and QA software. The display controller board is installed in a PACS workstation computer, connected to the display. The QA software helps to make and keep the displays DICOM compliant. The display uses LED backlight technology.
Here's an analysis of the provided text regarding the Barco Nio 3MP LED (MDNG-3220) medical display system. This document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than comprehensive de novo clinical trials for new technologies.
Important Note: The provided document describes a medical flat panel display system, not an AI or algorithm-based device. Therefore, many of the requested points related to AI/algorithm performance (e.g., sample sizes for test/training sets, ground truth establishment, MRMC studies, standalone performance) are not applicable to this type of device. The acceptance criteria and "study" described are for hardware performance validation and comparison to a predicate display, not for diagnostic accuracy of an AI.
1. Table of Acceptance Criteria and Reported Device Performance
| Modification to Device | Test Performed | Acceptance Criteria | Reported Device Performance |
|---|---|---|---|
| LED backlight instead of CCFL | Optical tests, DICOM calibration, Luminance Uniformity tests | Pass the test | Passed (device has similar or superior characteristics) |
| Different platform (including firmware) | Functional tests | Pass the test | Passed (device has similar or superior characteristics) |
| Additional DisplayPort video input | Functional tests | Pass the test | Passed (device has similar or superior characteristics) |
| Front sensor implementation | Front sensor qualification test | Maximum 5% deviation | Passed (device has similar or superior characteristics) |
| Other material of front filter | Impact test | There shall be no cracking of the enclosure. There shall be no live parts that become accessible. | Passed (device has similar or superior characteristics) |
| Other material for sheet metal parts | Vibration and drop tests | Pass the test | Passed (device has similar or superior characteristics) |
| Not explicitly tied to specific modification | EMC test (IEC 60601-1-2) | Compliance with IEC 60601-1-2 standard | Passed (device has similar or superior characteristics) |
Summary of Device Performance: The document states that "The tests showed that the device has similar or superior characteristics compared to the predicate device and did not reveal new issues of safety and performance."
2. Sample Size Used for the Test Set and Data Provenance
This is not applicable as the device is a medical display, not an AI algorithm processing diagnostic data. The "tests" refer to bench tests and functional performance evaluations of the hardware itself (e.g., optical properties, structural integrity), not evaluation against a dataset of medical images.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
This is not applicable. Ground truth, in the context of AI, refers to the definitive correct diagnosis or finding in a medical image dataset. For a display device, the "ground truth" is the specified engineering performance metric (e.g., luminance, uniformity), which is measured by test equipment, not human experts.
4. Adjudication Method for the Test Set
This is not applicable. Adjudication refers to methods used to resolve discrepancies in expert interpretations of medical images for ground truth establishment. This concept does not apply to the bench testing of a display monitor.
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, an MRMC comparative effectiveness study was not done. This type of study is relevant for AI-powered diagnostic tools that assist human readers. The Nio 3MP LED is a display monitor, not an AI tool.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done
This is not applicable. The device is a display monitor, not an algorithm.
7. The Type of Ground Truth Used
The "ground truth" for a display device's performance is typically established through engineering specifications and measurements using calibrated testing equipment. For example, luminance is measured with a photometer, and DICOM conformance is checked against the standard. It is not expert consensus, pathology, or outcomes data, as those relate to diagnostic content interpreted on the device, not the device's intrinsic performance.
8. The Sample Size for the Training Set
This is not applicable. The device is a hardware display, not an AI algorithm that requires a training set.
9. How the Ground Truth for the Training Set Was Established
This is not applicable. There is no training set for a display monitor.
In summary: The provided document is a 510(k) submission for a medical display device (monitor). The "acceptance criteria" and "study" refer to engineering-level bench tests and functional evaluations to demonstrate that the new device (Nio 3MP LED) is substantially equivalent to a previously cleared predicate device (Nio 3MP) in terms of safety and effectiveness, despite some technological changes (primarily the backlight technology, additional video input, and sensory components). The questions related to AI/algorithm performance, ground truth, and human reader studies are not relevant to this specific device or its regulatory submission.
§ 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).