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The ClearCanvas RIS/PACS is an image management system whose intended use is to provide scaleable DICOM compatible PACS solutions for hospitals and related institutions and sites, which will archive, distribute, retrieve and display images and data from all hospital modalities (such as CR, CT, DR, MR, and other devices) and information systems. This also includes the display of structured reports and mammography images that have been created according to DICOM."For Presentation", and will include standard features and other tools for analyzing mammography images.

Lossy compressed mammography images and digitized film screen images must not be used for primary image interpretations. Mammography images may only be interpreted using an FDA approved monitor that offers at least 5 mega-pixel resolution and meets other technical specifications approved by the FDA.

ClearCanvas RIS/PACS is a Picture Archiving and Communication (PACS) software system for the management and review of medical image data, and other digital images. Such data can be received from all DICOMcompliant imaging modalities or imported directly into the system. This data can then be stored, archived, distributed, processed, enhanced, and displayed for review. Enhancement, processing and analysis of the image data includes but is not limited to compression, magnification, value-of-interest manipulation, orientation changes, image fusion and multiplanar reformat. Additional health data, such as patient demographics, from other information systems can be received from HL7-compliant sources.

The ClearCanvas RIS/PACS is a system of interrelated software components:

• . ClearCanvas Workstation – a desktop client that is primarily used to retrieve, display, enhance and analyze medical images. Access to patient demographic data is also possible through the Workstation when working with the RIS server.
• . ClearCanvas Webstation – a browser-based web application that allows the user to retrieve images from the ImageServer for review. Primarily intended for referring physicians and as an image viewer for EMR systems, the images are JPEG-compressed.
• . ClearCanvas ImageServer – a server application that receives images from imaging modalities, stores and archives them, and distributes them to client applications.
• . ClearCanvas RIS – a server application that acts as an online transaction processor for the management of patient and workflow information.
• . ClearCanvas IntegrationServer – a server application composed of optional modules that implement integration and interoperability, such as an inbound and outbound HL7 processor.
• . ClearCanvas EnterpriseServer -- a server application that provides enterprise-wide facilities such as user authentication, authorization and auditing.

Working in concert or stand-alone, each component plays a valuable role in the management information in the radiology reading room, the clinic, the referring physician's office, or any other patient care setting where access to medical imaging information is important.

ClearCanvas RIS/PACS software is primarily written in C#, employing the most current best practices in objectoriented and component-oriented software architecture resulting in a highly scalable and extensible design. The system is also designed to work on generic PC hardware that meets the minimum system requirements.

This is a 510(k) premarket notification for the ClearCanvas RIS/PACS, which is a Picture Archiving and Communication System (PACS). The submission focuses on demonstrating substantial equivalence to predicate devices rather than providing a study with specific acceptance criteria and performance metrics for a novel AI algorithm. Therefore, much of the requested information regarding AI algorithm performance, sample sizes for training/test sets, expert adjudication, MRMC studies, and ground truth establishment is not present in this document.

The document primarily lists technological characteristics and intended uses to show equivalence to existing PACS systems.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't provide a table of quantitative acceptance criteria for device performance in the typical sense of AI/CADe devices (e.g., sensitivity, specificity, AUC). Instead, it relies on demonstrating that its technological characteristics and intended use are equivalent to legally marketed predicate devices. The "performance" is implicitly accepted if it matches or exceeds the predicate devices' capabilities in areas like image handling, display, and workflow.

2. Sample size used for the test set and the data provenance:

• Sample Size: Not applicable. This submission does not describe a clinical study with a "test set" in the context of evaluating an AI algorithm's performance on a specific disease detection task. The performance testing conducted was to verify design outputs met input requirements and validate conforming to user needs under simulated use conditions. No specific patient data sample size is mentioned for this testing.
• Data Provenance: Not applicable. No specific country of origin or retrospective/prospective nature of data is mentioned as the testing was likely functional and system-level, not clinical efficacy evaluation on patient datasets.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Number of Experts: Not applicable. There is no mention of experts or ground truth establishment in the context of evaluating diagnostic accuracy or performance on patient images. The device itself is a PACS, not a diagnostic AI tool.
• Qualifications of Experts: Not applicable.

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

• Adjudication Method: Not applicable. No adjudication process is described as there's no clinical performance study on patient data.

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:

• MRMC Study: No, an MRMC comparative effectiveness study was not done. The device is a PACS, not an AI-assisted diagnostic tool in the sense of a CADe/CADx system. The submission focuses on the PACS system's functionality and equivalence to other PACS systems.
• Effect Size: Not applicable.

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

• Standalone Performance: No. The ClearCanvas RIS/PACS is a "Picture Archiving and Communication (PACS) software system for the management and review of medical image data" and is explicitly described as being "used by trained and qualified professionals affording ample opportunity for competent human intervention in interpreting the images and information presented." It is not an algorithm designed for standalone diagnostic performance.

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

• Type of Ground Truth: Not applicable. No ground truth is mentioned in the context of clinical diagnostic accuracy. The "performance testing" referenced was likely functional and integration testing against defined system requirements, not against clinical outcomes or expert labels.

8. The sample size for the training set:

• Training Set Sample Size: Not applicable. This is not an AI/ML algorithm that requires a training set for model development.

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

• Training Set Ground Truth: Not applicable.

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