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Vitrea™2 is a medical diagnostic workstation that allows the processing, review, analysis, communication and media interchange of multi-dimensional digital images acquired from a variety of imaging devices. In addition, Vitrea™2 has the following specific indications.

VScore™ (K990442) is an option within the Vitrea™2 application and is intended for cardiac scoring from whole body CT derived measurements, including non-invasive detection and quantification of atherosclerotic plaque. Two image processing options, EKG Gate (K001682) and Auto Gate (K003230), allow the operator to select images with reduced motion artifacts when processing data for Coronary Artery Calcification Scoring.

Automated Vascular Measurement (K002519) is an option within the Vitrea™2 application and is intended for study/analysis of selected vessels for stenosis analysis, pre/post stent planning and directional vessel tortuosity evaluation.

Tumor Volume Measurement (K002519) is an option within the Vitrea™2 application and is intended for the analysis/quantification of tumor volumes obtained from MR brain series scans.

CT Brain Perfusion (K003639) is an option within the Vitrea™2 application and is intended for post processing based on dynamic CT images continuously acquired during the injection of contrast for the visualization of apparent blood flow in brain tissue and pictorial illustration of perfusion related parameters to aid in the type and extent of cerebral perfusion disturbances.

ImageCheckerCT is an option within the Vitrea™2 application and is intended for the display of a composite view of 2D crosssections, and 3D volumes of Chest CT images, including findings of regions of interest ("ROI") identified by the radiologist, or Computer Assisted Detection ("CAD") findings.

Vitrea™2 is a medical diagnostic workstation that allows the processing, review, analysis, communication and media interchange of multi-dimensional digital images acquired from a variety of imaging devices. Vitrea™2, version 3.4 is an upgrade to Vitrea™2, version 2.1 initially released for commercial distribution by FDA on K002519.

Vitrea 2 provides multi-dimensional visualization of digital images to aid clinicians in their analysis of anatomy and pathology. The Vitrea2 user interface follows typical clinical workflow patterns to process, review, and analyze digital images, including:

• Retrieve image data over the network via DICOM .
• Display images that are automatically adapted to exam type via dedicated protocols .
• Select images for closer examination from a gallery of up to six 2D or 3D views .
• . Interactively manipulate an image in real-time to visualize anatomy and pathology
• Annotate, tag, measure, and record selected views
• Output selected views to standard film or paper printers, or post a report to an Intranet Web server or . export views to another DICOM device
• Retrieve reports that are archived on a Web server .

Here's a breakdown of the requested information based on the provided text:

Acceptance Criteria and Device Performance Study

The provided document is a 510(k) Summary for Vitrea2, Version 3.4. It declares substantial equivalence of the device to predicate devices rather than providing detailed acceptance criteria and a performance study with quantitative metrics.

The document states: "The Vitrea2, Version 3.4 Workstation will successfully complete Integration testing/verification prior to Beta validation. The software Beta testing validating the workstation will be successfully completed prior to release." This indicates that internal testing and validation were conducted, but specific metrics, acceptance criteria, or the results of those tests are not provided in this summary.

Therefore, many of the requested details about acceptance criteria, specific performance metrics, sample sizes, and ground truth establishment for a standalone or comparative effectiveness study are not available in this document. The summary focuses on comparing the new device's intended use and technological characteristics to previously cleared predicate devices to establish substantial equivalence.

Based on the information available, here's what can be extracted:

1. Table of Acceptance Criteria and Reported Device Performance

Not explicitly provided in the 510(k) Summary. The document asserts the device's functionality and safety through comparison to predicate devices and general software development processes, but does not list specific quantitative acceptance criteria or corresponding performance scores for any of its features (VScore, Automated Vascular Measurement, Tumor Volume Measurement, CT Brain Perfusion, or ImageCheckerCT).

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

Not provided in the 510(k) Summary. There is no mention of a specific test set, its size, or the provenance of the data used for any validation studies.

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

Not provided in the 510(k) Summary. The document does not detail how ground truth was established for any testing, nor does it specify the number or qualifications of experts involved.

4. Adjudication Method for the Test Set

Not provided in the 510(k) Summary. There is no mention of an adjudication method for any test set.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No MRMC comparative effectiveness study is reported in this 510(k) Summary. The document focuses on demonstrating substantial equivalence to predicate devices, not on quantifying the improvement of human readers with AI assistance.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

No standalone performance study with specific metrics is reported in this 510(k) Summary. The document describes a medical diagnostic workstation with various processing options to aid clinicians, implying human interaction. It does not provide data on the pure automated performance of its features.

7. Type of Ground Truth Used

Not explicitly stated in the 510(k) Summary. Given the nature of the device (medical image processing software for diagnosis), if ground truth were used, it would typically be based on expertconsensus, pathological findings, or clinical outcomes, but the document does not specify this.

8. Sample Size for the Training Set

Not provided in the 510(k) Summary. There is no mention of a training set or its size. This document is from 2003, predating the widespread use and specific regulatory requirements for detailing AI/ML model training data that are common today.

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

Not provided in the 510(k) Summary. As no training set is mentioned, the method for establishing its ground truth is also not provided.

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