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CVV-001A, Vessel View Software is a post processing software option for the TSX-101A CT system. This product can be used for the analysis of CT angiography images. It provides display and measurement tools that can aid trained physicians for visualizing and assessing various blood vessels.

The CVV-001A will be added to the previously cleared TSX-101A Aquilion CT system. This addition requires software modifications to the existing device. Addition of this option will provide trained physicians with visualization and measurements tools for assessing blood vessels.

Here's an analysis of the provided 510(k) summary for the CVV-001A Vessel View Software, focusing on acceptance criteria and study details.

Important Note: The provided 510(k) summary (K063184) for the "CVV-001A, Vessel View Software" is very brief and primarily focuses on the device description, intended use, and substantial equivalence to a predicate device. It does not contain detailed information about specific acceptance criteria or the studies conducted to prove device performance against those criteria.

510(k) summaries often serve as a high-level overview. For detailed performance and validation data, one would typically need to review the full 510(k) submission, which is not publicly available in its entirety in this format.

Based on the limited information provided, I will construct the answers as best as possible, indicating when information is "Not Provided in the Document".

Acceptance Criteria and Reported Device Performance

Given the nature of the device (software providing visualization and measurement tools for blood vessels) and the predicate (GE CardIQ Analysis II), the acceptance criteria would likely revolve around accuracy of measurements, clarity of visualization, and equivalence to manual methods or predicate device performance. However, these are not explicitly stated in the provided document.

Study Details

1. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size for Test Set: Not Provided in the Document.
• Data Provenance: Not Provided in the Document (e.g., country of origin, retrospective/prospective). While "Toshiba America Medical Systems, Inc." is the submitter, the origin of clinical data is not specified.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Number of Experts: Not Provided in the Document.
• Qualifications of Experts: Not Provided in the Document.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Adjudication Method: Not Provided in the Document.

4. 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

• MRMC Study: It is highly unlikely that a typical MRMC comparative effectiveness study was performed as understood for AI-assisted diagnostic tools. The device is described as "visualization and measurements tools for assessing blood vessels" that "facilitate the assessment of blood vessels by a trained physician." This suggests it's an image processing and measurement tool, not necessarily an AI-driven detection or classification system that alters diagnostic accuracy. The 510(k) summary does not mention any study of this nature.
• Effect Size: Not Applicable / Not Provided in the Document.

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

• Standalone Performance: The document describes the device as providing "display and measurements tools" to "aid trained physicians." This indicates it's designed for human-in-the-loop use. A standalone performance evaluation in the context of an AI algorithm making independent decisions is not explicitly stated or implied by this description. Performance would likely be assessed for the accuracy of its measurements and displays, which are then used by a physician.

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

• Type of Ground Truth: Not Provided in the Document. For a measurement tool, ground truth would typically be established through highly accurate manual measurements (e.g., by experts using reference tools) or by comparison to other validated imaging modalities or invasive measurements, if applicable.

7. The sample size for the training set

• Sample Size for Training Set: Not Provided in the Document. Given the device description as "visualization and measurement tools," it's not explicitly stated that it uses machine learning/AI requiring a "training set" in the modern sense. It might be a rule-based or algorithmic image processing software. If it did involve training, the size is not mentioned.

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

• Ground Truth for Training Set: Not Provided in the Document. If a training set was used, the methodology for establishing its ground truth is not detailed.

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