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VR SOFTSTORE is a software device intended to direct the lossless archival storage and retrieval, including the use of pre-fetching and auto-routing capabilities, of digital medical images within a Picture Archiving and Communications System (PACS).

VR SOFTSTORE may be used for the permanent storage and subsequent retrieval of any medical imaging data presented in DICOM or other supported format, within a Picture Archiving and Communications System (PACS).

The VR NetServe software module is an addition to the VR SOFTSTORE medical image archive system which allows the user to access patient images using web server technology to query the database and display retrieved images. The intended use of the VR SOFTSTORE system containing this module has not been modified by this addition.

This 510(k) submission describes the VR NetServe, a software module that adds web access capabilities to the existing VR SOFTSTORE medical image archive system.

Here's an analysis of the provided information regarding acceptance criteria and study details:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not contain explicit acceptance criteria or a performance study in the typical sense for a medical device that analyzes patient data.

This submission is for a PACS component software (image storage device) that allows web access to archived medical images. The core claim is substantial equivalence to a predicate device (VR SOFTSTORE, ID.Store image archive software device) and that the addition of the VR NetServe module does not modify the intended use and does not pose any new issues of safety and effectiveness.

Therefore, the "acceptance criteria" here are implied by the FDA's substantial equivalence determination process for this type of device, which focuses on:

• Maintaining the intended use of the predicate device.
• Not introducing new technological characteristics that raise new questions of safety or effectiveness.
• Performing its described functions (lossless archival storage and retrieval of digital medical images, including web access).

Given this context, a table of acceptance criteria and reported performance would look like this:

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

The document does not describe a test set involving patient data or clinical images for performance evaluation. This device is a PACS component software primarily concerned with image storage, retrieval, and access. As such, a clinical performance study with a test set of patient data, as would be common for diagnostic AI, is not mentioned or required for this type of submission.

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

Not applicable. As no test set involving clinical interpretation was performed, no experts were used to establish ground truth for such a test set.

4. Adjudication Method for the Test Set

Not applicable. No test set involving clinical interpretation was performed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size

No. An MRMC study is relevant for devices that assist human readers in diagnostic tasks. This device is a PACS component for archiving and access, not a diagnostic aid.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

No. The device's primary function is to provide web access for archived images, not to perform independent analysis or diagnosis. Its performance measurement would likely be functional (e.g., successful image retrieval, network accessibility, data integrity) rather than diagnostic accuracy.

7. The Type of Ground Truth Used

Not applicable. For this type of device (PACS component for storage and access), "ground truth" in the diagnostic sense is not relevant. The system's "truth" lies in its ability to accurately store, retrieve, and display the original data without loss or corruption, and to function as described.

8. The Sample Size for the Training Set

Not applicable. This device is an image archiving and access software module, not an AI/ML algorithm that requires a training set.

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

Not applicable. This device is not an AI/ML algorithm requiring a training set with established ground truth.

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