Search Results

The system is designed to provide image storage, display, and workflow integration. The image display architecture provides capabilities for near-realtime capabilities for radiologists as well as image reviewing workgroup members and other clinicians. In addition to traditional 2D image viewing functionality, the image display system provides advanced 3D features including volume rendering and multi-planar reconstruction designed to function in web-enabled viewers over both local and wide area networks.

UltraVisual Vortex™ is an integrated client-server software package, which may be marketed as software only, that is used in conjunction with standard PC hardware. UltraVisual Vortex™ is a PC-based, DICOM-compliant PACS device that is able to receive, transmit and display DICOM images over both local and wide area network. Images sent to the UltraVisual Vortex™ server are converted into formats suitable for viewing in a web browser, and stored in a local cache (hard disk). The algorithms used by UltraVisual Vortex™ to create JPEG and wavelet images follow known and accepted protocols.

Vortex™ can be used within a hospital, a managed care facility or an isolated imaging center. It can also be used for image distribution over the network for teleradiology/review purpose.

UltraVisual Vortex™ uses standard "off-the-shelf" PC hardware and communicates using the standard TCP/IP stack.

Here's an analysis of the provided text regarding the acceptance criteria and study for the UltraVisual Vortex™ software, structured according to your request:

Acceptance Criteria and Study for UltraVisual Vortex™ Software

1. Table of Acceptance Criteria and Reported Device Performance

Based on the provided text, the "acceptance criteria" for the UltraVisual Vortex™ software are primarily focused on substantial equivalence to predicate devices and functional parity in terms of image handling and display capabilities. The document doesn't explicitly state quantitative performance metrics or acceptance criteria in the typical sense (e.g., minimum accuracy percentages). Instead, it demonstrates effectiveness by comparing its features and intended use to already cleared devices.

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

The provided text does not specify a sample size for a test set in terms of patient cases or images. It states: "Extensive testing of the software package has been performed by programmers, by non-programmers, quality control staff, and by potential customers." This suggests internal testing and perhaps user acceptance testing, but no formal, documented clinical test set of specific size is mentioned.

Therefore, the data provenance is also not explicitly stated in terms of country of origin or whether it was retrospective or prospective. It is implied that the testing involved internally generated data or data readily available to the development team.

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

The document does not mention the use of experts to establish ground truth for any specific test set. The validation focuses on functional equivalence and internal testing by various staff members, not on clinical performance or diagnostic accuracy validated against expert consensus.

4. Adjudication Method for the Test Set

Since no formal test set with ground truth established by experts is described, there is no adjudication method mentioned.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done

No, a multi-reader multi-case (MRMC) comparative effectiveness study is not mentioned in the provided text. The document does not describe any study comparing human readers with and without AI assistance.

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

The validation described is primarily a standalone functional validation of the software. It verifies that the algorithm can perform its stated functions (receive, convert, display images, perform 3D rendering, etc.) and that these functions are comparable to predicate devices. However, this is not a study of diagnostic performance or clinical effectiveness, but rather a verification of its technical capabilities. There is no mention of measuring the algorithm's diagnostic performance without human interaction.

7. The Type of Ground Truth Used

Given the nature of the device (an image processing, communication, and visualization workstation) and the described validation, the "ground truth" used would primarily be functional correctness and adherence to standards. For example:

• DICOM compliance: Verified by successfully processing DICOM 3.0 images.
• Image integrity: Verified that images are displayed correctly after conversion (e.g., JPEG and wavelet conversion following "known and accepted protocols").
• Feature functionality: Verified that tools like volume rendering, MPR, and MIP operate as designed and produce expected outputs for various inputs.
• Substantial equivalence: Verified by feature-by-feature comparison with predicate devices.

There is no mention of pathology, outcomes data, or expert consensus being used as ground truth for diagnostic accuracy, as this device primarily handles image viewing and manipulation, not automated diagnosis.

8. The Sample Size for the Training Set

The document does not mention a training set sample size. The Vortex™ software is described as using "known and accepted protocols" for image conversion and processing. This suggests that the algorithms are based on established methods rather than a machine learning model that would require a distinct training set.

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

Since there is no mention of a training set or machine learning algorithms in the context of a training phase, there is no information provided on how ground truth for a training set was established.

Ask a specific question about this device