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Quantib™ Brain is a non-invasive medical imaging processing application that is intended for automatic labeling, visualization, and volumetric quantification of segmentable brain structures from a set of magnetic resonance (MR) images. The Quantib™ Brain output consists of segmentations, visualizations and volumetric measurements of grev matter (GM), white matter (WM), and cerebrospinal fluid (CSF). The output also visualizes white matter hyperintensity (WMH) candidates. Users need to review and if necessary, edit WMH candidates using the provided tools, before validation of the WMHs. It is intended to provide the trained medical professional with complementary information for the evaluation and assessment of MR brain images and to aid the trained medical professional in quantitative reporting. Quantib™ Brain is a post-processing plugin for the GE Advantage Workstation (AW 4.7) or AW Server (AWS 3.2) platforms.

Quantib™ Brain is post-processing analysis software for the GE Advantage Workstation (AW 4.7) and AW Server (AWS 3.2) platforms using Volume Viewer Apps. 12.3 Ext 8 (or higher). It is intended for automatic labeling, visualization, and volumetric quantification of identifiable brain structures from magnetic resonance images (a 3D T1-weighted MR image, with an additional T2-weighted FLAIR MR image for white matter hyperintensities (WMH) segmentation system relies on a number of atlases each consisting of a 3D T1-weighted MR image and a label map dividing the MR image into different tissue segments. Quantib™ Brain provides quantitative information on both the absolute and relative volume of the segmented regions. The automatic WMH segmentation is to be reviewed and if necessary, edited by the user before validation of the segmentation, after which volumetric information is accessible. Longitudinal analysis can be performed for the brain tissue segmentation and WMH segmentation in order to compare multiple exams of an individual patient. Quantib Brain is intended to provide the trained medical professional with complementary information for the evaluation and assessment of MR brain images and to aid the radiology specialist in quantitative reporting.

Here's a breakdown of the acceptance criteria and study information for Quantib™ Brain 1.2, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance:

The document primarily focuses on the validation of a newly added algorithm for classifying White Matter Hyperintensities (WMH) as consistent, new, or disappearing between longitudinal scans. The overall performance of core segmentation algorithms (brain volumetry and WMH) is stated as unchanged from the predicate device.

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

• Test Set Sample Size: 12 datasets.
• Data Provenance: The document states "12 datasets of different subjects, each consisting of a baseline exam and 1 to 3 follow-up exams." It does not explicitly state the country of origin or if the data was retrospective or prospective. However, given the nature of longitudinal studies, it's highly likely to be retrospective data collected over time from individual patients.

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

The document states "manual labeling of these WMHs" was used for comparison. It does not specify the number of experts or their qualifications (e.g., radiologist with X years of experience).

4. Adjudication Method for the Test Set:

The document mentions "manual labeling" for comparison but does not detail an adjudication method (e.g., 2+1, 3+1, none) among multiple experts, suggesting the ground truth was established by a single manual labeling process, or the details of such a process are not provided.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done:

No, an MRMC comparative effectiveness study is not mentioned in the provided text. The evaluation focuses on the algorithm's standalone performance compared to manual labeling, not on human reader performance with or without AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done:

Yes, a standalone performance evaluation of the new WMH labeling algorithm was done. The study assessed the automatic labeling of WMHs against manual labeling, without a human-in-the-loop component.

7. The Type of Ground Truth Used:

The ground truth used for the WMH labeling algorithm was expert manual labeling. The document states, "The automatic labeling of WMHs was for 99.6% of the WMH volume identical to manual labeling of these WMHs."

8. The Sample Size for the Training Set:

The document does not provide any information regarding the training set sample size for the new WMH labeling algorithm or for the existing core algorithms.

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

The document does not provide any information on how the ground truth for the training set was established, as details about the training set itself are absent.

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