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Omni-Vue 2™ System is a software device that receives digital images and data from various sources (e.g. CT scanners, MR scanners, ultrasound systems, R/F Units, computed & direct radiographic devices, secondary capture devices, scanners, imaging gateways or other imaging sources). Images and data can be stored, communicated, processed and displayed within the system and or across computer networks at distributed locations. Device options make possible reading (including mammography), telecommunications; fast demonstration; etc .; and teleconferencing.

Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretation. Mammographic images may only be interpreted using an FDA cleared monitor that meets technical specifications reviewed and accepted by FDA

Omni-Vue 2™ System makes possible the capturing, storage, distribution, manipulation, and networking of medical images at distributed locations. In cases where DICOM images are not directly available, the System can acquire medical images using a DICOM gateway, which generates DICOM-type files. For example, film digitizers obtain images from old film and convert them to meet DICOM standards and stored files are transmitted using a network and can be viewed or manipulated from an imaging workstation.

The provided K130374 510(k) summary for the Omni-Vue 2™ System details the device's characteristics and its substantial equivalence to a predicate device. However, it does not contain specific acceptance criteria, a detailed study proving the device meets these criteria, or information regarding sample sizes for test/training sets, ground truth establishment, or expert involvement as requested.

The document primarily focuses on establishing substantial equivalence by comparing the features of the Omni-Vue 2™ System to its predicate device, OmniVue, and demonstrating that any differences do not impact safety or efficacy. The "Nonclinical Testing" section mentions that "predetermined acceptance criteria were met" but does not specify what those criteria were or detail the studies performed to meet them.

Therefore, most of the requested information cannot be extracted from this document.

Here's what can be inferred or stated based on the provided text, with explicit notes where information is missing:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not specified. The document mentions "nonclinical testing results" but does not provide details on the number of images or cases used in the testing.
• Data Provenance: Not specified. It doesn't mention country of origin or whether the data was retrospective or prospective.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The document generally states that "A physician, providing ample opportunity for competent human intervention interprets images and information being displayed and printed," but this refers to the intended use of the device, not the ground truth establishment for testing.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified.

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 comparative effectiveness study is mentioned. This device is a PACS system, not an AI-assisted diagnostic tool, so such a study would not be applicable in this context.

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

• No standalone performance study is mentioned or applicable, as this is a software system intended for human interaction and interpretation.

7. The Type of Ground Truth Used

• Not specified. The document refers to "nonclinical testing results" and meeting "predetermined acceptance criteria" but does not elaborate on the nature of the ground truth used for these internal tests.

8. The Sample Size for the Training Set

• Not applicable. This document describes a Picture Archiving Communications System (PACS), which is a software device for managing and viewing medical images. It does not involve a machine learning algorithm that requires a training set.

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

• Not applicable, as there is no training set for this type of device.

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Omni-Vue™ System is a software device that receives digital images and data from various sources (e.g. CT scanners, MR scanners, ultrasound systems, R/F Units, computed & direct radiographic devices, secondary capture devices, scanners, imaging gateways or other imaging sources). Images and data can be stored, communicated, processed and displayed within the system and or across computer networks at distributed locations. Device options make possible reading (including mammography), telecommunications; fast demonstration; etc.; and teleconferencing. Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretation. Mammographic images must only be interpreted using an FDA approved monitor that offers at least 5 Mpixel resolution and meets other technical specifications reviewed and accepted by FDA.

The Omni-Vue device allows limited image processing capability: "sharpening preset" and window leveling function.

For image editing; the only an "L" or "R" marker can be added to the image but it must be already defined in the original image DICOM header as received by the system. "L" or "R" can not be added to the image if left & right is not defined in the original image.

The device provides scout line which is a reference line and is drawn on the AP image to display the location of the slice being viewed. In multi series images, whenever one image is clicked, scout line will be drawn on other series which is crossed direction.

Omni-Vue™ System makes possible the capturing, storage, distribution, manipulation, and networking of medical images at distributed locations. In cases where DICOM images are not directly available, the System can acquire medical images using a DICOM gateway, which generates DICOM-type files. For example, film digitizers obtain images from old film and convert them to meet DICOM standards and stored. Stored files are transmitted using a network and can be viewed or manipulated from an imaging workstation.

The provided text does not contain specific acceptance criteria or a detailed study proving the device meets them in the format requested. The document is a 510(k) summary indicating the device's substantial equivalence to a predicate device, Omni-Vue™ System, as a Picture Archiving Communications System (PACS).

Here's a breakdown of why the requested information cannot be fully provided based on the given text:

1. Table of Acceptance Criteria and Reported Device Performance: This information is not present. The document focuses on demonstrating substantial equivalence to a predicate device, not on specific performance metrics against pre-defined acceptance criteria.
2. Sample Size for Test Set and Data Provenance: Not mentioned.
3. Number of Experts and Qualifications for Ground Truth: Not mentioned.
4. Adjudication Method: Not mentioned.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study: Not mentioned. The document describes a software device for image management and viewing, not an AI-assisted diagnostic tool that would typically undergo such studies to show improvement in human reader performance.
6. Standalone Performance Study: Not explicitly mentioned as a formal study with metrics. The "Technological Characteristics" section states that "A physician, providing ample opportunity for competent human intervention interprets images and information being displayed and printed," implying human oversight rather than an algorithm-only review.
7. Type of Ground Truth Used: Not mentioned.
8. Sample Size for Training Set: Not mentioned. This device is a PACS system, not a machine learning algorithm that typically requires a training set.
9. How Ground Truth for Training Set Was Established: Not applicable as it's a PACS system.

Summary of available information related to performance/evaluation:

The core of the submission is to demonstrate substantial equivalence to the predicate device, PACSPartner™ (K042311). This typically involves showing that the new device has the same intended use, similar technological characteristics, and that any differences do not raise new questions of safety or effectiveness.

The document mentions:

• "The submission contains the results of a hazard analysis and the 'Level of Concern for potential hazards has been classified as 'Minor'."
• The device "will be manufactured in accordance with the voluntary standards listed in the enclosed voluntary standard survey."

For a PACS system like Omni-Vue™, performance criteria would typically relate to:

• Image Quality: Maintaining the diagnostic quality of images.
• Speed and Efficiency: How quickly images can be acquired, stored, transmitted, and displayed.
• Security: Protection of patient data.
• Reliability: System uptime and data integrity.
• Functionality: Successful implementation of features like "sharpening preset," "window leveling," adding 'L'/'R' markers (when defined in DICOM header), and scout lines.

However, the provided text does not explicitly state how these types of performance criteria were measured or quantified for the Omni-Vue™ System in a formal study. The FDA's determination of "substantial equivalence" is based on the comparison to the predicate device, assuming similar performance for similar technology.

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