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syngo.plaza is a Picture Archiving and Communication System intended to display, process, read, report, communicate, distribute, store, and archive digital medical images, including mammographic images. It supports the physician in diagnosis and treatment planning. For primary image diagnosis in Mammography only uncompressed or non-lossy compressed images and only preprocessed DICOM "For Presentation" images must be used. Also monitors (displays) and printers which received FDA clearance for Mammography must be used. syngo.plaza also supports DICOM Structured Reports. In a comprehensive imaging suite, syngo.plaza integrates Hospital / Radiology Information Systems (HIS/RIS) to enable customer specific workflows. syngo.plaza optionally uses a variety of advanced postprocessing applications. Note: Web-based image distribution is not intended for reporting.

syngo plaza is a "software only"-system, which will be delivered on CD-ROM / DVD to be installed on common IT hardware. syngo.plaza will be installed by Siemens service engineers. The backend communication and storage solution is based in Windows 2008 operating systems as described in the predicate devices syngo.plaza VA10A (093612) and syngo.x (K092519). The clients are based on Windows XP as also described in the predicate devices syngo.plaza VA10A (093612) and syngo.x (K092519) The syngo plaza will be marketed as a software only solution for the end-user (with recommended hardware requirements). It will be installed by trained service engineers only. Any special needs such as integration in a specific environment and updates / upgrades will be I any spond individual service contract and fulfilled by special trained service technicians. This is also applicable for the predicate devices syngo.plaza VA10A (093612) and syngo.x (K092519). The herewith described syngo plaza VA20A supports DICOM formatted images and This which is also described for the predicate devices syngo.plaza VA10A (093612), syngo.x (K092519) and syngo Imaging (K071114).

The provided text describes the syngo.plaza VA20A, a Picture Archiving and Communication System (PACS). However, it does not contain information about specific acceptance criteria or a study proving that the device meets those criteria, as typically found in performance studies for AI/CADe devices.

The document is a 510(k) summary for a PACS system, which primarily focuses on stating substantial equivalence to predicate devices based on intended use and technical characteristics, rather than detailed performance metrics of a novel algorithm or diagnostic aid.

Here's a breakdown of what is and is not present, based on your request:

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

• Not provided. The document describes the system's functionalities (display, process, read, report, communicate, distribute, store, and archive digital medical images) and states that software verification and validation were performed according to various standards (DICOM, JPEG, ISO, IEC, HL7). It concludes that "Siemens is of the opinion, that syngo plaza is substantially equivalent to and performs as well as the predicate device." There are no quantitative performance metrics (e.g., sensitivity, specificity, AUC) or defined acceptance criteria for such metrics.

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

• Not provided. No specific test set, sample size, or data provenance is mentioned as this is not a clinical performance study of an AI algorithm.

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 provided. Ground truth establishment is not relevant in this context, as there's no clinical performance study reported.

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

• Not provided. Not applicable.

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 provided. No MRMC study is mentioned. The device is a PACS system, not an AI or CADe product designed to assist human readers in a diagnostic task in a comparative effectiveness study.

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

• Not provided. Not applicable, as this is a PACS system and not a standalone AI algorithm with diagnostic output.

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

• Not provided. Not applicable.

8. The sample size for the training set:

• Not provided. Not applicable, as this is a PACS system, not an AI algorithm that undergoes specific training on a dataset.

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

• Not provided. Not applicable.

In summary, the provided document is a regulatory submission (510(k) summary) for a PACS system (syngo.plaza VA20A) demonstrating its substantial equivalence to previously cleared predicate devices. It focuses on functional descriptions, compliance with technical standards, and risk management, rather than detailed performance studies of an AI diagnostic component as would be required for a device claiming to interpret images or assist in diagnosis via AI/CAD.

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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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