(106 days)
The VidiStar PACS & DICOM Viewer Software system is a picture archiving and communications system (PACS) intended to be used as a networked Digital Imaging and Communications in Medicine (DICOM) and non-DICOM information and data management system. The VidiStar PACS & DICOM Viewer Software is comprised of modular software programs that run on standard "off-the-shelf" personal computers, business computers, and servers running standard operating systems. VidiStar PACS & DICOM Viewer Software system is an image, data storage and display software that accepts DICOM data from laboratories, which support DICOM standard imaging data and structured reporting transfer(s). The system provides the capability to: organize images generated by OEM vendor equipment, perform digital manipulation, create graphical representations of anatomical areas, perform quantitative measurements, and create DICOM structure reports, all over the Internet.
All quantitative data ranges are derived from the clinical experience of laboratories and are included in observation libraries for VidiStar users. VidiStar strongly recommends that users review these ranges with their individual diagnostic needs in mind prior to using the VidiStar PACS & DICOM Viewer Software system for clinical reporting. The VidiStar PACS & DICOM Viewer Software system should not be used for reviewing full-field digital mammograms.
The VidiStar PACS & DICOM Viewer Software System is a picture archiving and communications system software used to process, display, transfer, enable reports, communicate, store and archive digital medical images using Transmission Control Protocol/Internet Protocol (TCP/IP). It supports DICOM structured reports for creating, rendering, storage and archiving.
The provided text describes the VidiStar PACS & DICOM Viewer Software System and its substantial equivalence to other PACS devices on the market. However, it does not contain information about specific acceptance criteria, a detailed study proving the device meets those criteria, or the methodology (sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, or ground truth establishment) typically associated with such studies for AI/CAD devices.
The document is a 510(k) summary focused on demonstrating "substantial equivalence" to predicate devices, which is a regulatory pathway for medical devices. This pathway often relies on comparing features and performance to existing, legally marketed devices rather than presenting novel clinical performance studies with acceptance criteria in the manner requested.
Therefore, most of the requested information cannot be extracted from the provided text.
Here's what can be inferred or explicitly stated from the document:
1. A table of acceptance criteria and the reported device performance
The document does not specify formal "acceptance criteria" for clinical performance. Instead, it demonstrates substantial equivalence by comparing features to predicate devices. The "performance" is implied by matching or exceeding the capabilities of the predicate devices.
| Feature | Acceptance Criteria (Implied by Predicate) | Reported VidiStar PACS & DICOM Viewer Software Performance |
|---|---|---|
| Operating System | Windows NT/2000/2003/XP | Linux and Windows 2000/XP |
| Image Source | DICOM | DICOM |
| Display Rates | Over 30 fps | Over 30 fps |
| Multiple Windows | Yes | Yes |
| Image Export | bmp, jpg, mpg, avi | bmp, jpg, png, avi |
| Network Access | Yes | Yes |
| Analysis | Yes | Yes |
| Reporting | Yes | Yes |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
Not mentioned. The 510(k) summary focuses on design control activities and comparison to predicates, not a specific clinical performance test set.
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)
Not mentioned. Ground truth establishment for a specific test set is not detailed as there is no described clinical performance study of this nature.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not 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
No MRMC study is mentioned. The device is a PACS and DICOM viewer, not an AI/CAD algorithm intended to assist human readers in a diagnostic capacity that would be evaluated by such a study in this document.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. The device is a PACS system and viewer, not a standalone algorithm with diagnostic performance. Its function is to process, display, store, and manage images.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
Not applicable for a clinical performance study as none is described for specific diagnostic tasks. The "ground truth" for the device's functionality would be adherence to DICOM standards and correct display/storage of images, which would be verified through functional testing (ALPHA, BETA testing), not clinical ground truth as defined for diagnostic AI.
8. The sample size for the training set
Not mentioned. A training set is typically associated with machine learning or AI algorithms, which is not the primary focus or nature of this PACS software as described for regulatory submission.
9. How the ground truth for the training set was established
Not mentioned, as no training set is described.
Summary of available information:
The document describes the VidiStar PACS & DICOM Viewer Software System as a networked PACS intended for processing, displaying, storing, and managing DICOM and non-DICOM medical images and data. It outlines design control activities like validation planning and ALPHA/BETA testing. The core of its regulatory submission relies on demonstrating substantial equivalence to existing PACS products by comparing features such as operating system, image source, display rates, multiple window support, image export formats, network access, analysis capabilities, and reporting features. No specific clinical performance study with acceptance criteria, sample sizes, expert ground truth, or AI-specific evaluations (like MRMC or standalone performance) is detailed in this 510(k) summary.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).