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N-VAS-D is designed and intended for use as a supporting tool to non-invasive assessment of the vascular system. Intended purposes are:

• Supporting clinical diagnoses about the flow velocity and volume flow through the vascular system.
• Supporting subsequent clinical decision making purposes.
• Supporting clinical post-operation and follow-up evaluation about the flow velocity and volume flow throughout the vascular system.
• Supporting the use in the treatment planning using computer modeling of the vascular system.

N-VAS-D is a software tool used to non-invasively measure blood flow in the vascular system. N-VAS-D works on the images acquired from an MRI. It uses Time-of-Flight MRI images obtained via digital network to generate a 3D image. N-VAS-D allows fast scan acquisition time, fast post processing, and accurate flow measurement. N-VAS-D provides accurate vessel identification y using stereo visualization. N-VAS-D gives velocity and volume flow as a function of time, and other derived data such as mean velocity and volumetric flow rate. N-VAS-D generates a web browser compatible flow report that shows both flow results and images.

The N-VAS-D 2.0 includes three modules: 3DP, 3DFLOW and AUTOREPORT. N-VAS-D works on the images acquired from an MRI. It uses Time-of-Flight MRI images obtained via digital network to generate a 3D image. N-VAS-D allows fast scan acquisition time, fast post processing, and accurate flow measurement. N-VAS-D provides accurate vessel identification by using stereo visualization. N-VAS-D gives velocity and volume flow as a function of time, and other derived data such as mean velocity and volumetric flow rate. N-VAS-D generates a web browser compatible flow report that shows both flow results and images.

The provided text describes a software device (N-VAS-D 2.0) intended for neuro-vascular analysis. However, it does not include detailed acceptance criteria or a specific study proving the device meets acceptance criteria in the format requested.

The document primarily focuses on regulatory information, including:

• The device's name, classification, and intended use.
• A list of predicate devices for substantial equivalence.
• A general statement about testing conducted ("Software Verification and Validation including...") and that the "Proposed device has successfully met the requirements of the above."
• The FDA's letter of substantial equivalence determination.

Therefore, many of the requested details about acceptance criteria, specific performance metrics, sample sizes, expert qualifications, and study methodologies cannot be extracted from the provided text.

Here's an attempt to answer based on the available information, with significant caveats where information is missing:

1. Table of Acceptance Criteria and Reported Device Performance

• Note: The document states, "Proposed device has successfully met the requirements of the above." However, it does not specify what those requirements/acceptance criteria were (e.g., "flow velocity within X% of ground truth," "ROI repeatability to within Y pixels"). It only lists high-level categories of testing.

2. Sample size used for the test set and the data provenance

• Information Not Provided. The document does not specify any sample sizes for test sets, nor does it mention data provenance (country of origin, retrospective/prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Information Not Provided. The document does not describe the establishment of a "ground truth" for a test set or the involvement of experts in that process.

4. Adjudication method for the test set

• Information Not Provided. No adjudication method is mentioned.

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

• Information Not Provided. The document does not mention any MRMC study or comparative effectiveness study involving human readers with or without AI assistance. The device is described as a "supporting tool," but no studies on its impact on human performance are detailed.

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

• Partially Addressed, but Lacking Detail: The listed "Testing conducted" (e.g., flow velocity, flow rate, ROI repeatability) implies standalone performance testing of the algorithm's capabilities. However, the specific metrics, methodologies, and results beyond "successfully met requirements" are not provided.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• Information Not Provided. The document does not specify the type of ground truth used for any of the performance assessments mentioned.

8. The sample size for the training set

• Information Not Provided. The document does not mention a training set or its size.

9. How the ground truth for the training set was established

• Information Not Provided. As no training set is mentioned, the method for establishing its ground truth is also not provided.

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