(62 days)
Visia™ Neuro is a medical image processing software application intended for the visualization of images from various sources such as Magnetic Resonance Imaging systems or from image archives. The system provides viewing, quantification, manipulation, and printing of medical images. Visia™ Neuro provides both analysis and viewing capabilities for anatomical and physiologic/functional imaging datasets, including blood oxygen dependent (BOLD) fMRI, diffusion, fiber tracking, dynamic review, and vessel visualization. Data can be visualized in both 2D and 3D views.
BOLD fMRi Review: The BOLD MRI feature is useful in identifying small susceptibility changes arising from neuronal activity during performance of a specific task.
Diffusion Review: The diffusion review feature is intended for visualization and analysis of the diffusion of water molecules through brain tissue.
Fiber Tracking Review: The fiber tracking feature uses the directional portion of the diffusion vector to track and visualize white matter structures within the brain.
Dynamic Review: Dynamic review feature is intended for visualization and analysis of MRI dynamic studies, showing changes in contrast over time, where such techniques are useful or necessary.
Vessel Visualization: The vessel feature is used to identify and visualize the vascular structures of the brain.
3D Visualization: The 3D visualization feature allows image data to be reconstructed as 3D objects that are visualized and manipulated on a 2D screen.
Visia™ Neuro is a medical image processing software application intended for the visualization of images from various sources such as Magnetic Resonance Imaging systems or from image archives. The system provides viewing, quantification, manipulation, and printing of medical images.
Visia™ Neuro integrates within typical clinical workflow patterns through receiving and transferring medical images over a computer network. The software can be loaded on a standard off-the-shelf personal computer (PC) and can operate as a stand-alone workstation or in a distributed server-client configuration across a computer network.
The software provides functionality for processing and analyzing both anatomical and physiologic/functional imaging datasets. Specifically, the software includes user defined processing modules for image registration, blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI), diffusion imaging, fiber tracking, dynamic imaging, and vessel imaging. Processed images are stored as separate files from the original data such that the original data is preserved.
Images may be displayed based on physician preferences using configurable layouts, or hangings. Visia™ Neuro provides the clinician with a broad set of viewing and analysis tools in both 2D and 3D. The software includes tools to annotate, measure, and output selected image views or user defined reports.
The provided documentation for Visia™ Neuro does not contain specific acceptance criteria or a detailed study that proves the device meets such criteria in terms of quantitative performance metrics for medical diagnosis or image interpretation.
Instead, the submission focuses on demonstrating substantial equivalence to a predicate device (DC Neuro, K081262) through non-clinical testing and verification/validation activities of the software itself. The document states that the software passed "all in-house testing criteria" and that "the results demonstrated that the predetermined acceptance criteria were met." However, these acceptance criteria are not explicitly defined in terms of clinical performance (e.g., accuracy, sensitivity, specificity for identifying pathologies).
Here's a breakdown of the information that is available in the provided text:
1. Table of Acceptance Criteria and Reported Device Performance:
No specific clinical acceptance criteria for diagnostic performance (e.g., sensitivity, specificity, AUC) are mentioned. The document primarily discusses functional and technical acceptance criteria related to software performance and safety.
| Acceptance Criteria Category | Reported Device Performance |
|---|---|
| Software Functionality | Passed all in-house testing criteria for input functions, output functions, and actions in each operational mode. |
| Safety and Effectiveness | Risk management procedures identified potential hazards, which were controlled via software development and verification & validation testing. |
| Technological Characteristics (Substantial Equivalence) | Substantially equivalent to the predicate device (DC Neuro, K081262) in technical characteristics, general function, application, and intended use. Does not raise new safety risks. |
2. Sample size used for the test set and the data provenance:
- Test Set Description: The document refers to "the complete system configuration" being "assessed and tested at the manufacturer's facility." It also mentions "Validation Test Plan" results.
- Sample Size: Not specified. It only refers to "all verification activities."
- Data Provenance: Not specified, but given it was "in-house testing," it's likely internal, potentially simulated or based on historical data readily available to the manufacturer. It doesn't specify if it's retrospective or prospective.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- The document states: "Diagnosis is not performed by the software but by Radiologists, Clinicians and referring Physicians." and "A physician, providing ample opportunity for competent human interprets images and information being displayed and printed."
- However, for the validation testing of the software itself, there's no mention of experts establishing ground truth for evaluating diagnostic performance. The validation appears to be focused on software functionality and technical aspects rather than clinical outcome.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
Not applicable or not mentioned, as the validation described is for software functionality and not for diagnostic accuracy requiring expert adjudication.
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. The document describes a software medical device for visualization and analysis, not an AI-assisted diagnostic tool requiring MRMC studies to assess human reader improvement.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
The device is explicitly described as a tool for visualization and analysis, with diagnoses made by physicians. Therefore, a "standalone algorithm only" performance study in a diagnostic context is not relevant to its intended use as described. The software's performance was evaluated in terms of its functions, not its diagnostic accuracy.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
No ground truth type is specified for evaluating the device's clinical performance because the validation described is centered on software functionality and technical equivalence.
8. The sample size for the training set:
Not applicable. This is a medical image processing software application, not a machine learning or AI algorithm that requires a "training set" in the context of learning to perform a diagnostic task.
9. How the ground truth for the training set was established:
Not applicable, as there is no mention of a training set for an AI/ML algorithm.
§ 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).