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syngo Imaging CCF is a Picture Archiving and Communication System intended for enterprise wide storage, archiving and distribution of digital radiology images and reports to clinical personnel for viewing of current and prior studies. It is not intended for primary diagnosis.

This premarket notification addresses the Siemens syngo-based Picture Archiving and Communication System syngo® Imaging CCF VA10A.

syngo Imaging CCF is a Picture Archiving and Communication System intended for enterprise wide storage, archiving and distribution of digital radiology images and reports to clinical personnel for viewing of current and prior studies. It is not intended for primary diagnosis.

It offers the possibility to store the images in uncompressed/lossless compressed and lossy compressed format depending on modality specific configuration to optimize usage of storage space.

All lossy compressed images are displayed with warning triangles to indicate that the images are not suitable for diagnosis. In DICOM transfers, the respective compression related DICOM attributes are filled.

In a comprehensive imaging suite, syngo Imaging integrates hospital information systems (HIS) to enable customer specific workflows.

The system is a "hardware independent" solution to be distributed combined with common IT hardware which must comply to predefined minimum hardware requirements.

syngo® Imaging CCF VA10A supports the following additional features:

• NAS based central enterprise storage of 10x compression
• Data replication between sites in a multisite environment
• Automatic data reconciliation (in synch with information systems)
• Client redirectin - for high availability and to enable cross reading
• Enterprise patient context (multi MRN support)
• Comprehensive server performance monitoring

Basically these functionalities do not alter the fundamental scientific technology of syngo® Imaging CCF VA10A.

syngo® Imaging (CCF VA10A) is a "software plus hardware"-system, which will be delivered on CD-ROM / DVD or as a complete radiology solution consisting of common IT hardware and preinstalled software. syngo® Imaging CCF_VA10A will be installed by Siemens service engineers.

Defined Hardware requirements are to be met.

The backend communication and storage solution (DM) is based on LINUX and Windows 2003 operating system. The workplaces are based on Windows XP. Any hardware platform, which is Windows / Windows XP certified, will be supported.

The herewith described syngo® Imaging (CCF VA10A) supports DICOM formatted images and objects.

This document (K073418) is a 510(k) premarket notification for a Picture Archiving and Communication System (PACS) named syngo® Imaging CCF VA10A. It is not a study reporting on the performance of a new diagnostic device. The purpose of this 510(k) is to demonstrate substantial equivalence to previously cleared devices rather than to prove that the device meets specific acceptance criteria through a performance study in the way a diagnostic AI would.

Therefore, many of the requested sections about acceptance criteria, clinical study details, and reader performance are not applicable to this submission. The device is intended for "enterprise-wide storage, archiving and distribution of digital radiology images and reports to clinical personnel for viewing," and not for primary diagnosis. As such, the typical performance metrics for diagnostic accuracy (sensitivity, specificity, AUC) are not relevant here.

Here's a breakdown of the information that is available or derivable from the provided text, and where the requested information is not applicable:

1. Table of Acceptance Criteria and Reported Device Performance

Not applicable in the context of a 510(k) for a PACS system intended for archiving and viewing, not primary diagnosis. The "performance" is primarily related to its functional capabilities and safety, which are addressed through substantial equivalence.

The document states, "syngo Imaging CCF is a Picture Archiving and Communication System intended for enterprise wide storage, archiving and distribution of digital radiology images and reports to clinical personnel for viewing of current and prior studies. It is not intended for primary diagnosis."

Key functional characteristics and related "performance" claims are:

• Storage of images in uncompressed/lossless compressed and lossy compressed formats.
• Warning triangles displayed for lossy compressed images to indicate they are not suitable for diagnosis.
• Integration with hospital information systems (HIS).
• Multisite data replication.
• Automatic data reconciliation.
• Client redirection for high availability and cross-reading.
• Enterprise patient context (multi MRN support).
• Comprehensive server performance monitoring.
• Compliance with DICOM conformance statement for PACS server use.
• Integration with HL7-/DICOM-compatible RIS.

These are not quantitative performance metrics that would have specific acceptance criteria reported in a table in this type of submission.

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

Not applicable. This 510(k) submission does not describe a clinical performance study using a test set of patient data to evaluate diagnostic accuracy. Its purpose is to demonstrate substantial equivalence to predicate devices for its intended use as an archiving and viewing system.

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

Not applicable. As no clinical performance study for diagnostic accuracy was conducted or reported in this submission, there was no "ground truth" to establish for a test set in that context.

4. Adjudication Method for the Test Set

Not applicable. No test set requiring adjudication for diagnostic performance was 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. This device is a PACS for archiving and viewing, not an AI-powered diagnostic tool. No MRMC study was conducted or is relevant to this submission.

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

Not applicable. This device is a PACS system, not a standalone diagnostic algorithm. Its function is to display images for human review, not to make diagnostic decisions independently.

7. The Type of Ground Truth Used

Not applicable. As no diagnostic performance study was conducted, no "ground truth" (e.g., pathology, expert consensus, outcomes data) was established for this device's evaluation.

8. The Sample Size for the Training Set

Not applicable. This device is a software system for image management and viewing. It is not an AI/ML model that would require a "training set" of patient data in the typical sense for learning diagnostic patterns. Its development would involve software engineering and testing against functional requirements and standards, not a training set for an algorithm.

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

Not applicable. As there is no training set for an AI/ML diagnostic algorithm, there is no ground truth establishment methodology described.

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