Search Results

nordicBrainEx is an advanced visualization and processing software, with specific focus on providing algorithms designed to analyze functional MR data of the brain. The software runs on a standard "offthe-shelf" PC workstation and can be used with data and images acquired through DICOM compliant imaging devices and modalities.

The software is intended to be used by medical personnel, such as radiologists or medical technicians, trained in the methods provided by the application. In order to best accommodate this group of users, it is specifically designed to have an easy to use and streamlined workflow, as well as an intuitive graphical user interface.

nordicBrainEx provides analysis and visualization capabilities of dynamic MRI data of the brain, presenting the derived properties and parameters in a clinically useful context.

The nordicBrainEx Software is a post-processing application for dynamic MRI data developed with focus on ease of use and high performance on a standard Windows workstation. The software provides comprehensive functionality for dynamic image analysis and visualization of MRI data, where signal changes over time are analyzed to determine various modality dependent functional parameters. The following algorithms provide the main functional analyses of the application.

• BOLD: BOLD fMRI analysis is used to highlight small magnetic susceptibility changes in the human brain in areas with altered blood-flow resulting from neuronal activity.
• DTI: Diffusion analysis is used to visualize local water diffusion properties from the analysis of diffusion-weighted MRI data. Fiber tracking utilizes the directional dependency of the diffusion to display the white matter structure in the brain.
• DSC: Calculations of perfusion related parameters that provide information about the blood vessel structure and characteristics. Examples of such maps are blood volume, blood flow, time to peak, mean transit time and leakage.

In addition to these specific functional analyses, the application also provides general visualization tools, a database for data handling, and a reporting feature. This is to ensure that the workflow of the application is optimized to ensure efficiency and high throughput in a clinical environment.

The nordicBrainEx Software is a post-processing application designed for dynamic MRI data, focusing on the analysis and visualization of functional MR data of the brain. The device's performance was assessed through extensive in-house testing to ensure it meets design and performance specifications and user needs.

Here's the breakdown of the acceptance criteria and the study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text states that "Prospectively defined verification and validation activities for the nordicBrainEx Software assure that the nordicBrainEx Software meets design and performance specifications as well as user needs when operated according to the operating instructions." However, specific quantitative acceptance criteria (e.g., sensitivity, specificity, accuracy thresholds) or detailed reported device performance metrics are NOT explicitly stated in the document. The document primarily focuses on demonstrating substantial equivalence to a predicate device (nordicICE Software K090546) based on technological and operational characteristics.

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size: Not specified. The document mentions "extensive in-house testing" but does not provide details on the number of cases or datasets used for verification and validation.
• Data Provenance: Not specified. The document does not mention the country of origin of the data or whether it was retrospective or prospective. It only states that the software "can be used with data and images acquired through DICOM compliant imaging devices and modalities."

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

Not specified. The document does not mention the use of experts to establish a ground truth for testing, nor their number or qualifications.

4. Adjudication Method for the Test Set

Not specified. No information is provided regarding any adjudication method (e.g., 2+1, 3+1, none) used for the test set.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done

No. The document does not mention any Multi Reader Multi Case (MRMC) comparative effectiveness study. The evaluation focused on the device's technical performance and substantial equivalence to a predicate device, not on assessing human reader improvement with AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

The performance testing described as "extensive in-house testing" for "design and performance specifications" and "user needs" suggests an evaluation of the algorithm's functionality and output. While not explicitly termed "standalone performance," the context implies that the algorithms for BOLD, DTI, and DSC analyses were tested for their ability to produce the described parameters. However, no specific standalone metrics (e.g., accuracy, precision) are provided. The device is intended to be used by medical personnel, indicating a human-in-the-loop context for clinical application, but the verification tests would inherently assess the algorithm's output quality.

7. The Type of Ground Truth Used

Not specified explicitly. Given the nature of the device (post-processing for functional MRI data), ground truth would typically come from established physiological models, other validated imaging techniques, or potentially comparison with manually segmented/analyzed images by experts. However, the document does not specify how ground truth was established for its internal testing.

8. The Sample Size for the Training Set

Not applicable/Not specified. The nordicBrainEx Software is described as a post-processing application with defined algorithms (BOLD, DTI, DSC). The text implies algorithm-based analysis rather than a machine learning model that would require a separate training set. Therefore, a "training set" in the context of machine learning is not mentioned or implied.

9. How the Ground Truth for the Training Set Was Established

Not applicable/Not specified. As there is no mention of a training set, the establishment of its ground truth is also not mentioned.

Ask a specific question about this device