(25 days)
syngo® Dynamics is a Picture Archiving and Communication System (PACS) intended for acceptance, transfer, display, storage, archive and manipulition of digital medical images, including quantification and report generation.
syngo® Dynamics is not intended to be used for reading of mammography images.
This premarket notification covers Siemens' enhanced system syngo® Dynamics, version 6.0.
syngo® Dynamics is a digital image management system that includes a DICOM server. This system receives, stores, distributes, and archives images from digital image acquisition devices such as ultrasound and x-ray anglography machines. The system has workplaces that can be used to review, edit, and masinilate image data, as well as to generate quantitative data, qualitative data, and diagnostic reports.
syngo® Dynamics provides advanced reporting features, including cardiology oriented features for review and analysis of x-ray angiographic images, and the capability to launch 3rd party software applications either as stand alone application or via Internet Explorer,
syngo® Dynamics is a software device that is shipped as a turn key system with pre-installed software on common, off-the-shelf OEM computer hardware. syngg® Dynamics is installed by Siemens service engineers.
Version 6.0 contains extended features for cardiac cath viewing and reporting as well as for cardiac echo reporting. Further on syngo® Dynamics 6.0 includes enhanced integration of the Siemens Sequoia Ultrasound Product and the Siemens Axiom Sensis product for reporting in the cath lab environment.
syngo® Dynamics, version 6.0 also offers an optional "software only" workplace with full viewing and report generation, which will be delivered on CD-ROM and installed by the end user on his own computer hardware.
Here's an analysis of the provided text regarding the acceptance criteria and study details for the syngo® Dynamics (version 6.0) device.
However, it's important to note that the provided 510(k) summary (K070322) for syngo® Dynamics (version 6.0) does NOT contain information about specific performance acceptance criteria or a study designed to prove the device meets those criteria, as one might find for a diagnostic or AI-driven CAD device. This document is a Class II device clearance for a PACS system, which primarily focuses on image management, storage, and display functionality. The regulatory pathway for such devices often emphasizes substantial equivalence to predicate devices and adherence to general safety and effectiveness concerns rather than specific clinical performance metrics like sensitivity or specificity.
Therefore, many of the requested details, particularly those related to clinical performance studies, ground truth establishment, expert review, and sample sizes for training/test sets, are not present in the provided text.
The information below reflects what can be extracted from the document, with explicit notation where information is missing.
1. Table of Acceptance Criteria and Reported Device Performance
As stated above, this 510(k) summary does not outline specific, quantifiable performance acceptance criteria (e.g., sensitivity, specificity, accuracy) typical of diagnostic or AI-driven devices, nor does it report such performance metrics. The clearance is based on substantial equivalence for a Picture Archiving and Communication System (PACS).
The "acceptance criteria" for this type of device primarily revolve around:
- Functional equivalence to predicate devices: Performing the same functions (acceptance, transfer, display, storage, archive, manipulation of digital medical images, quantification, report generation) as cleared predicate devices.
- Adherence to standards: Compliance with DICOM (Digital Imaging and Communications in Medicine) standards.
- Software validation and verification: Ensuring the software performs as intended without introducing new safety risks.
- Safety assessment: Risk management to identify and control potential hazards.
Therefore, a table of acceptance criteria and reported performance, in the sense of clinical metrics, cannot be directly constructed from this document.
| Acceptance Criterion (Inferred from PACS Classification) | Reported Device Performance (Inferred from Substantial Equivalence Claim) |
|---|---|
| Functional Equivalence | |
| Acceptance, transfer, display, storage, archive of digital medical images | syngo® Dynamics performs these functions, including "enhanced features for cardiac cath viewing and reporting as well as for cardiac echo reporting," and "pediatric hemodynamics, electro-physiology, and enhanced adult cath reporting." |
| Manipulation of image data | Capable of manipulating image data. |
| Quantification and report generation | Provides "advanced reporting features" and "report generation." |
| Support for DICOM-formatted images and structured report objects | Supports DICOM-formatted images and structured report objects. |
| Not for mammography reading | syngo® Dynamics is "not intended to be used for reading of mammography images." |
| Safety & Effectiveness | |
| No new potential safety risks | "Siemens is of the opinion that syngo® Dynamics does not introduce any new potential safety risks." |
| Performance as well as predicate devices | "performs as well as the predicate devices." |
| Risk management | Risk analysis used to identify and control potential hazards via "software development and verification and validation testing." |
| Compliance with industry practices & standards | "Siemens adheres to recognized and established industry practices and standards" to minimize electrical, mechanical, and radiation hazards. |
2. Sample Size for the Test Set and Data Provenance
- Sample Size for Test Set: Not specified. The document describes software verification and validation testing, but does not quantify a "test set" in terms of patient cases or images for clinical performance evaluation.
- Data Provenance: Not specified. Given it's a PACS system and the assessment focuses on functional equivalence and safety, the "data" likely refers to simulated or representative medical images and related data used for software testing, rather than a clinical dataset for performance evaluation.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications
- Number of Experts: Not specified.
- Qualifications of Experts: Not specified.
- Explanation: For a PACS system, "ground truth" as typically understood in AI or diagnostic device evaluation (e.g., confirming a diagnosis) is not the primary focus. Development and testing would focus on accurate data handling, display, and workflow, which might involve domain experts (e.g., radiologists, cardiologists) for usability and functional validation, but not typically for establishing diagnostic "ground truth" for an algorithm's performance.
4. Adjudication Method for the Test Set
- Adjudication Method: Not specified.
- Explanation: Since a clinical performance test set with diagnostic "ground truth" is not described, an adjudication method for reconciling expert opinions would not be applicable in the context presented.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done
- Was an MRMC study done? No, not mentioned in the document.
- Effect size of human readers with/without AI assistance: Not applicable, as no MRMC study or AI assistance is discussed in the context of improving human reader performance.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done
- Was a standalone study done? No. The device is a PACS system, which supports human clinical workflow. While it has components for "quantification," these are tools for human interpretation, not an autonomous diagnostic algorithm.
7. The Type of Ground Truth Used
- Type of Ground Truth: Not specified in the context of clinical performance.
- Explanation: For a PACS, "ground truth" in testing would generally relate to the correctness of data storage, retrieval, display accuracy (e.g., does the image display correctly?), and functional execution (e.g., does the measurement tool calculate correctly?). This relies on adherence to standards (DICOM) and internal system logic rather than external expert consensus, pathology, or outcomes data for diagnostic accuracy.
8. The Sample Size for the Training Set
- Sample Size for Training Set: Not applicable. The document does not describe an AI or machine learning component that would require a "training set" in the context of learning to make diagnostic predictions. This is a traditional software system.
9. How the Ground Truth for the Training Set was Established
- How Ground Truth for Training Set was Established: Not applicable, as there is no mention of a training set or an AI/ML component.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).