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VINT is a software that allows for the viewing, post-processing, and quantitative evaluation of cerebrovascular MRI data (in DICOM-compliant format).

It enables:

• Importing DICOM-compliant MR Images.

• Quantitative analysis of imported images.

• Visualization and analysis of blood flow characteristics of cerebral arteries in MR images.

• Advanced correction options such as offset correction, background phase correction, and anti-aliasing.

• Data visualization as a graph or output as an image or numerical data.

• Providing the right and left carotid and vertebral arteries wall signal intensity gradient (SIG), an image-based indirect marker for velocity gradient from TOF-MRA.

VINT is intended for use with adult (22 years or more) populations who can undergo MRA.

VINT is a standalone medical software device designed for processing, analyzing, viewing, and quantifying cerebral artery MR images. The software is intended to visualize and quantify MRI data imported in DICOM format. The key functions of the VINT software are 2D and 3D viewing, multiphase series support, zoom, pan, window level, rotate, ROI Markers: Ability to create preset shapes or freehand ROI for measurements or segmentations, provide heatmaps to help users understand the distribution of arterial signal intensity gradient at a glance, and show measured numbers in a table.

It can also be used to analyze multi-slice signal intensity gradient of a TOF-MRA to visualize and analyze the blood flow characteristics of cerebral arteries, without any-contrast medium for MR angiography.

Here's a breakdown of the acceptance criteria and the study information based on the provided text, structured according to your request:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance for Test Set

The document explicitly mentions:

• Human studies: Yes, for evaluating the correlation between TOF-MRA SIG and PC-MR velocity gradient.
• Tubal experiments: Yes, for evaluating the correlation between TOF-MRA SIG and PC-MR velocity gradient.

Data Provenance: The document does not specify the country of origin for the human studies or whether they were retrospective or prospective.

3. Number of Experts and Qualifications for Ground Truth

The document does not specify the number of experts used or their qualifications for establishing ground truth for the test set.

4. Adjudication Method

The document does not describe any specific adjudication method (e.g., 2+1, 3+1, none) for the test set.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned. The study involved a comparative validation between the subject device and a reference device for the correlation of SIG from TOF-MRA with PC-MR velocity gradient. It also performed bench testing against Predicate Device A for general performance metrics. There is no information provided about human readers improving with or without AI assistance as part of this study.

6. Standalone Performance Study (Algorithm Only)

The document describes "Bench testing" and "comparative validation between the subject device and the reference device" which evaluates the device's technical performance. This implicitly suggests a standalone evaluation of the algorithm's output against a reference. Specifically, it states: "Bench tests were performed using the subject device and the reference device (K213583) to validate TOF-MRA SIG as a reliable measure of shear rate obtained from PC-MR data."

7. Type of Ground Truth Used

• For evaluating the correlation between TOF-MRA SIG and velocity gradient: PC-MR data served as the ground truth. The document states, "The SIG from TOF-MRA showed highly significant correlations with velocity gradient calculated from the PC-MR, both in tubal experiments and human studies," implying PC-MR velocity gradient was the reference for comparison.

8. Sample Size for Training Set

The document does not provide information about the sample size used for the training set.

9. How Ground Truth for Training Set Was Established

The document does not provide information on how ground truth was established for the training set. Given that the VINT device is described as "Medical Image Management And Processing System" and focuses on viewing, post-processing, and quantitative evaluation, it's possible that traditional "training" in the machine learning sense might not be explicitly detailed if the core functionality is based on established image processing algorithms. However, no specifics are given.

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