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The EnSite Verismo™ Segmentation Tool (EV 1000) is indicated for use in generating 3D models from slice-based DICOM3 image data. Generated models are intended to be displayed on the EnSite® System.

The EnSite Verismo™ Segmentation Tool is designed to function on the EnSite System's display workstation. This software tool allows importation of DICOM slice data from a variety of CT and MRI manufacturers. Once imported into the EnSite System, this slice data can be segmented into a 3D surface model. This model can be displayed during EP studies conducted on the EnSite System.

The provided document is a 510(k) summary for the EnSite Verismo™ Segmentation Tool (EV1000). It focuses on establishing substantial equivalence to a predicate device and does not contain detailed information on acceptance criteria or a comprehensive study demonstrating performance against specific metrics. It primarily addresses the device's intended use, technological characteristics, and non-clinical performance data in a general sense.

Therefore, much of the requested information regarding specific acceptance criteria, detailed study parameters, and quantitative performance metrics is not available in the provided text.

Here's an analysis of what can be extracted and what is missing:

1. A table of acceptance criteria and the reported device performance

Missing Information: The document does not specify quantitative acceptance criteria (e.g., specific accuracy, precision, sensitivity, or specificity thresholds) that the device needed to meet. The "reported device performance" is a high-level statement about the tests performed and the conclusion of substantial equivalence rather than granular performance metrics.

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

Missing Information: The document states that "device validation testing was conducted," but it does not provide any details about the sample size of the test set, the provenance of the data (e.g., country of origin), or whether the study was retrospective or prospective.

3. 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)

Missing Information: This information is not provided. The document mentions "user tests" but does not detail the number or qualifications of experts involved in establishing ground truth or evaluating the device's performance in these tests.

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

Missing Information: The document does not describe any adjudication method used for a test set.

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

Missing Information: There is no mention of a multi-reader multi-case (MRMC) comparative effectiveness study being performed, nor any data on how human readers might improve with or without AI assistance. The device is a "Segmentation Tool" to generate 3D models from DICOM data, which implies it's a tool for image processing and visualization rather than a diagnostic AI that would typically be evaluated in an MRMC study for reader improvement.

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

Provided Information: The document states, "The EnSite Verismo Software underwent a battery of bench and user tests." This implies that both standalone (bench tests) and human-in-the-loop (user tests) evaluations were conducted. However, no specific details or results for either are provided.

Missing Information: The results and specific methodology of the "standalone" bench tests are not detailed.

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

Missing Information: The document does not specify the type of ground truth used for any validation or testing.

8. The sample size for the training set

Missing Information: The document mentions "device validation testing" but does not refer to a "training set" or its size. This is common for a 510(k) submission where the focus is often on verifying the device's functionality and safety rather than a detailed AI model development and training process. The device's function as a "Segmentation Tool" suggests it might be based on deterministic algorithms or established image processing techniques rather than a machine learning model requiring a large training set in the modern sense.

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

Missing Information: Since there is no mention of a training set, the method for establishing its ground truth is also not provided.

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