(68 days)
syngo® Dynamics is a Picture Archiving and Communication System (PACS) intended for acceptance, transfer, display, storage, archive and manipulation 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 addresses the Siemens syngo® Dynamics version 9.0 Picture Archiving and Communication System.
syngo® Dynamics is intended to display, process, read, report, communicate, distribute and store digital medical images. The system is a "software only" medical device. It defines recommended requirements to the hardware it runs on.
The hardware itself is not considered a medical device and not in the scope of this 510(k) submission.
syngo® Dynamics supports the physician in diagnosis and treatment planning. It also supports storage and archiving of DICOM Structured Reports. In a comprehensive imaging suite syngo® Dynamics integrates Hospital / Radiology / Cardiology Information Systems (HIS/RIS/CIS) to enable customer specific workflows.
The syngo® Dynamics new release focuses on support of web based reporting. Also, in syngo® Dynamics 9.0, server as well as the workplaces will be offered as "software-only".
The provided text is a 510(k) summary for the syngo® Dynamics (version 9.0) Picture Archiving and Communication System (PACS). This document primarily describes the device, its intended use, and its substantial equivalence to a predicate device, focusing on regulatory aspects rather than detailed performance studies or elaborate acceptance criteria for specific features beyond general safety and effectiveness.
Here's an analysis based on the provided text:
1. A table of acceptance criteria and the reported device performance
The document states: "Predefined acceptance criteria was met and demonstrated that the device is as safe and effective as the predicate device." However, it does not provide a specific table of acceptance criteria or reported device performance metrics related to diagnostic accuracy, processing speed, or other quantitative performance measures that would typically be found in a detailed validation study. The acceptance criteria appear to be related to demonstrating substantial equivalence to the predicate device and adherence to general safety and effectiveness concerns through risk management, software development, verification, and validation testing.
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 provide information on the sample size used for any test set or the data provenance. The focus is on the software itself and its conformance to standards and risk management.
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)
The document does not provide information on the number of experts, their qualifications, or how ground truth was established for a test set.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
The document does not provide information on any adjudication method.
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
The document does not indicate that an MRMC comparative effectiveness study was conducted. The device is a PACS system for displaying and managing images, not an AI-assisted diagnostic tool in the sense of providing automated interpretations or improving human reader performance in a controlled study.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The document describes syngo® Dynamics as a "software only" medical device that supports physicians in diagnosis and treatment planning by displaying, processing, and storing images. It does not perform "standalone" diagnostic tasks in the absence of human interpretation; rather, it provides the tools for human professionals. Thus, a standalone performance study in the context of diagnostic accuracy would not be applicable to this kind of device.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
The document does not specify the type of ground truth used. Given the nature of the device as a PACS, the ground truth would typically relate to the accuracy of image display, storage, and retrieval, rather than diagnostic accuracy against a clinical reference.
8. The sample size for the training set
The document does not provide information on the sample size for a training set. This is not a machine learning or AI-driven diagnostic device that typically employs training sets.
9. How the ground truth for the training set was established
The document does not provide information on how ground truth for a training set was established, as it does not describe a training set in the context of machine learning.
§ 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).