(23 days)
syngo.via View&GO is a software solution intended to be used for Viewing, communication, and storage of medical images. It can be used as a stand-alone device or together with a variety of cleared and unmodified syngo based software options.
syngo.via View&GO supports interpretation of examinations within healthcare institutions, for example, in Radiology, Nuclear Medicine and Cardiology environments. The system is not intended for the displaying of digital mammography images for diagnosis in the US.
Siemens Healthcare GmbH intends to market the Picture Archiving and Communications System, syngo.via View&GO, software version VA10A. This 510(k) submission describes several modifications to the previously cleared predicate device, syngo.via, software version VB10A.
syngo.via View&GO is a software only medical device, which is delivered by download to be installed on common IT hardware. This hardware has to fulfil the defined requirements. Any hardware platform that complies to the specified minimum hardware and software requirements and with successful installation verification and validation activities can be supported. The hardware itself is not seen as part of the medical device syngo.via View&GO and therefore not in the scope of this 510(k) submission.
syngo.via View&GO provides tools and features to cover the radiological tasks preparation for reading, reading images and support for reporting 4. syngo.via View&GO supports DICOM formatted images and objects.
syngo.via View&GO is a standalone viewing and reading workplace. This is capable of rendering the data from the connected modalities for the post processing activities. syngo.via View&GO provides the user interface for interactive image viewing and processing with a limited short term storage which can be interfaced with any Long term storage (e.g. PACS) via DICOM.
syngo.via View&GO is based on Microsoft Windows operating systems.
syngo.via View&GO supports various monitor setups and can be adapted to a range of image types by connecting different monitor types.
The subject device and the predicate device share fundamental scientific technology.
The provided text describes a 510(k) premarket notification for syngo.via View&GO (Version VA10A), a Picture Archiving and Communications System (PACS). However, it does not contain the detailed information required to answer all parts of your request, specifically regarding a clinical study with detailed acceptance criteria, sample sizes, expert involvement, and ground truth establishment.
The document primarily focuses on demonstrating substantial equivalence to a predicate device (syngo.via VB10A) through comparisons of intended use, technological characteristics, and non-clinical performance testing. It highlights that syngo.via View&GO is a simplified version of the predicate device, with some functionalities removed.
Here's a breakdown of what can be extracted and what is missing:
1. A table of acceptance criteria and the reported device performance
The document states: "The testing results support that all the software specifications have met the acceptance criteria." However, it does not provide a specific table of acceptance criteria or quantitative performance metrics for the device’s functionality. It mentions "non-clinical tests were conducted for the device syngo.via View&GO during product development" to assess functionality, but the results themselves are not detailed in terms of specific performance against defined criteria.
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
This information is not provided in the document. The testing described is "non-clinical" and focuses on software verification and validation, not clinical performance using a specific test set of patient data.
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)
This information is not provided as the document does not describe a clinical study with expert-established ground truth.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This information is not provided as the document does not describe a clinical study.
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
This information is not provided. The device is a PACS system for viewing, manipulation, communication, and storage of medical images, and "no automated diagnostic interpretation capabilities like CAD are included." Therefore, an MRMC study assessing AI assistance is not applicable to this device based on the provided information.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
The document primarily describes a "software only medical device" that is a "standalone viewing and reading workplace" intended for use by medical professionals. The software itself is a standalone system in terms of its architecture (compared to the client-server predecessor), but its intended use involves human interpretation. It does not describe a standalone algorithmic performance study in the context of diagnostic interpretation, as it explicitly states it has no CAD functionalities.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
This information is not provided as there is no description of a clinical study that required ground truth establishment. The testing mentioned is "non-clinical" for software functionality.
8. The sample size for the training set
This information is not provided. The document describes a PACS system which displays existing medical images. It does not mention any machine learning or AI components that would require a "training set" of data in the typical sense for diagnostic algorithms.
9. How the ground truth for the training set was established
This information is not provided, as there is no mention of a training set or ground truth establishment for such a set.
Summary of what is available from the document:
The provided document describes a non-clinical performance testing approach for a PACS system, syngo.via View&GO. The "study" (non-clinical testing) aims to demonstrate that modifications to a previously cleared predicate device (syngo.via VB10A) do not introduce new safety risks and that the new device remains substantially equivalent for its intended use.
- Acceptance Criteria & Performance: The document states that "all the software specifications have met the acceptance criteria" based on non-clinical verification and validation testing. However, specific quantitative acceptance criteria and detailed performance metrics are not provided.
- Study Type: Non-clinical software verification and validation testing.
- Sample Size/Data Provenance: Not applicable for a typical clinical test set as described. The testing focuses on software functionality, not clinical performance on a dataset of patient cases.
- Expert/Ground Truth/Adjudication: Not applicable, as it's a non-clinical software validation without diagnostic AI features.
- MRMC Study: Not applicable, as the device does not have AI diagnostic interpretation capabilities.
- Standalone Performance: The device is described as a "standalone viewing and reading workplace" in terms of its architecture, but not in the context of an algorithm performing diagnoses without human involvement.
- Training Set/Ground Truth for Training: Not applicable/not mentioned, as there are no AI components requiring training.
§ 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).