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Stroke VCAR is a CT image analysis software package that provides information to physicians to assist them in the analysis and visualization of Brain CT data derived from DICOM 3.0 compliant CT scans. Stroke VCAR is designed for the purpose of segmenting and assessing intracerebral and intracranial hemorrhages in the brain using semi-automated tools on non-contrast CT exams. Additionally Stroke VCAR provides a set of workflow tools for the segmentation and visualization of aneurysms in the brain from contrast enhanced CT exams. It is intended for use by clinicians to process, review, archive, print and distribute CT studies.

This software will assist the user by providing initial 3D segmentation, measurements and visualization of hemorrhages and aneurysm in the brain. The user has the ability to adjust, review and has to confirm the final segmentation.

Stroke VCAR is intended to provide 2D and 3D processing, review and analysis of CT images originally acquired to evaluate the cerebral vascular system and/or intracranial bleeding. The combination of the acquired images, reconstructed images, and measurements performed by the clinician using Stroke VCAR are intended to provide the referring physician clinically relevant information for the purpose of diagnosis, treatment planning and follow-up.

Here's an analysis of the provided text regarding the Stroke VCAR device, focusing on acceptance criteria and the supporting study:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not explicitly state numerical acceptance criteria in terms of performance metrics (e.g., accuracy, sensitivity, specificity, Dice score, etc.) for the Stroke VCAR device. Instead, the "Summary of Clinical tests" section details observed benefits and qualitative feedback. The closest "performance" reported relates to workflow time improvement.

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

The text states: "A clinical evaluation study using consented clinical images was conducted..."

• Sample Size for Test Set: The specific number of cases (clinical images) used in the study is not explicitly stated. It mentions "consented clinical images," but gives no numerical quantity.
• Data Provenance (e.g., country of origin, retrospective or prospective): "Consented clinical images" suggests real-world data, likely retrospective, but the country of origin is not specified.

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

• Number of Experts: Three board-certified neuroradiologists were used in Part 2a and Part 2b of the study.
• Qualifications of Experts: They were identified as "board certified neuroradiologists" and "considered experts."

4. Adjudication Method for the Test Set

The document does not explicitly state an adjudication method (such as 2+1 or 3+1 consensus) for establishing the ground truth or evaluating the device's output. It mentions "three board certified neuroradiologists" but not how their assessments were combined or reconciled.

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

The study described does not appear to be a traditional MRMC comparative effectiveness study in the sense of comparing reader performance with and without AI assistance on diagnostic accuracy metrics.

Instead, the study's stated goals were:

• "Productivity benefits of automated vs. manual hematoma segmentation (Part 1)"
• "General user Qualitative feedbacks of the Stroke VCAR edition tools (Part 2a and Part 2b)"

While it compares automated vs. manual workflow time, it doesn't quantify diagnostic accuracy improvement for human readers using the AI. Therefore, an effect size of human reader improvement with vs. without AI assistance is not reported.

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

The provided text does not explicitly describe a standalone performance study of the algorithm without human-in-the-loop. The description states: "This software will assist the user by providing initial 3D segmentation, measurements and visualization of hemorrhages and aneurysm in the brain. The user has the ability to adjust, review and has to confirm the final segmentation." This indicates that the device is intended for semi-automated use where the human user confirms the results, implying that standalone performance may not be the primary evaluation focus here.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

Given the involvement of "three board certified neuroradiologists" who "were considered experts" in the clinical evaluation, the ground truth for evaluating the device's output (specifically for "diagnostic value" and potentially the accuracy of the automated segmentations) was likely based on expert consensus or expert interpretation of the clinical images. There's no mention of pathology or outcomes data as ground truth.

8. The Sample Size for the Training Set

The provided text does not specify the sample size for the training set used to develop the Stroke VCAR software. The "Summary of Non-Clinical Tests" mentions that "Stroke VCAR optimizes the segmentation algorithms for Hematoma segmentation and Aneurysm segmentation," implying training, but no details on the training data are given.

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

The provided text does not describe how the ground truth for the training set was established.

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