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Universal Viewer is a software application that displays medical image data and associated clinical reports to aid in diagnosis for healthcare professionals. It performs operations relating to the transfer, storage, display, and measurement of image data.

Typical users of this system are authorized healthcare professionals.

Mammography images may only be interpreted using a monitor compliant with requirements of local regulations and must meet other technical specifications reviewed and accepted by the local regulatory agencies.

Lossy compressed mammographic images and digitized film screen images should not be reviewed for primary image interpretations with use of the Universal Viewer.

Universal Viewer is an Internet based medical image display and interpretation software product that is part of a medical image management and processing system. It provides users with capabilities relating to the acceptance, transfer, display, and digital processing of medical images.

The Universal Viewer product does not produce any original medical images displayed by Universal Viewer have been received from DICOM compliant modalities and/or image acquisition systems.

Universal Viewer supports DICOM SOP classes to access and manage medical imaging studies from Computed Tomography (CT), Magnetic Resonance (MR), Ultrasound (US), Nuclear Medicine (NM), Computerized Radiography (CR), Digital mammography (MG), Digital X-ray (DX), Positron Emission Tomography (PET/PT), X-Ray Angiography (XA), Digital Intra-oral X-Ray (IO), Radiofluoroscopic X-ray (RF), Secondary Capture Images (SC), Visible Light (VL) Endoscopic, Microscopic and Photographic Image Storage, Slide Coordinates Microscopic Image Storage, Presentation States (PS), Key Image Notes (KIN), and other DICOM imaging modalities.

Universal Viewer provides image manipulation tools to enable users to view and compare images such as: measurements (linear distances, angles, areas, SUV, etc.), annotations (outline and label regions of interest, label spinal vertebrae), MPR, MIP, and 3D image fusion of CT, PET and registration of CT, PET and MR.

Universal Viewer is designed to be deployed over conventional Transmission Control Protocol/Internet Protocol (TCP/IP) networking infrastructure available in most healthcare organizations and utilizes commercially available computer platforms and operating systems.

Universal Viewer provides Application Program Interfaces (APIs) to integrate with third-party medical devices and non-medical devices.

This document is a 510(k) summary for the Universal Viewer, a medical image management and processing system developed by GE Healthcare. It states that the device is substantially equivalent to a predicate device (Centricity Universal Viewer K182419). As such, the document primarily focuses on demonstrating substantial equivalence rather than providing detailed acceptance criteria and a study to prove they are met in the same way a de novo device might.

Based on the provided text, here's the information related to acceptance criteria and the study that proves the device meets them:

1. A table of acceptance criteria and the reported device performance

The document does not provide a specific table of quantitative acceptance criteria and reported device performance metrics in the format typically seen for a new device's efficacy study. Instead, it relies on demonstrating that the Universal Viewer operates in a "substantially equivalent" manner to its predicate device, Centricity Universal Viewer (K182419).

The "Comparison" table in the document summarizes the feature/technological comparison between the predicate and proposed device, indicating functional equivalence:

The "Determination of Substantial Equivalence" section states:
"The testing and results did not raise new questions of safety and effectiveness from those associated with predicate device and demonstrated that the Universal Viewer device performs substantially equivalent to the predicate device."

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document does not specify a distinct "test set" with a particular sample size or data provenance in terms of patient data. The evaluation appears to be based on design control testing and comparison of functionalities to the predicate device, rather than a clinical study evaluating diagnostic performance on a patient dataset.

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 applicable, as no clinical study with explicit ground truth establishment by experts on a patient image test set is described. The evaluation focuses on technological and functional equivalence.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable, as no clinical study with a test set requiring adjudication is described.

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

Not applicable. The Universal Viewer is described as a medical image display and interpretation software, not an AI-powered diagnostic assist tool that would typically undergo an MRMC study to compare reader performance with and without AI assistance. The document focuses on the system's ability to display and process images, and its substantial equivalence to another such system.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

Not applicable. The device is a "medical image management and processing system" used by "healthcare professionals" to aid in diagnosis. It is not an autonomous algorithm designed for standalone performance.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

Not applicable, as no clinical study directly assessing diagnostic accuracy against a defined ground truth (e.g., pathology, expert consensus) is described. The "ground truth" for this type of submission is the established performance and safety of the predicate device, against which the new device is compared in terms of features and functionality.

8. The sample size for the training set

Not applicable. This document describes a software application for viewing and processing medical images, not an AI/ML model that would require a distinct training set. The development involved "design control testing" rather than model training.

9. How the ground truth for the training set was established

Not applicable, as there is no mention of a training set or ground truth in the context of machine learning.

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