(36 days)
syngo.MR Neurology is a software solution to be used for viewing and evaluation of Neuroperfusion MR images for the routine use in MR image viewing.
It is a syngo via based software option with dedicated MR specific workflows and basic MR specific evaluation tools and thus supports interpretation and evaluation of examinations within healthcare institutions, for example in Radiology, Neuroradiology and Neurosurgery environments.
syngo.MR Neurology is a post-processing software/application to be used for viewing and evaluating neurological MR images provided by a magnetic resonance diagnostic device. syngo.MR Neurology is a syngo.via-based software that enables structured evaluation of MR neurological images.
The medical device syngo.MR Neurology comprises syngo.MR Neuro fMRI (Neuro functional evaluation) and syngo.MR Neuro Perfusion Engine. syngo.MR Neuro Perfusion Engine comprises syngo.MR Neuro Perfusion (Perfusion and Local as well as Global AIF (Arterial Input Function)) and syngo.MR Neuro Perfusion Mismatch (Perfusion-Diffusion Mismatch Evaluation). This bundling is done for purchase purposes. Each application can also be purchased separately.
- syngo.MR Neuro Perfusion enables: processing of brain perfusion datasets acquired with DSC imaging. It provides color display and calculation of perfusion maps based on Arterial Input Function (AIF) (relative Mean Transit Time (relMTT), relative Cerebral Blood Volume (relCBV), and relative Cerebral Blood Flow (relCBF)).
- syngo.MR Neuro Perfusion Mismatch performs a calculation of the area differences between perfusion-diffusion datasets.
- syngo.MR Neuro fMRI is a workflow-oriented visualization package for BOLD fMRI.
The provided text is a 510(k) summary for the syngo.MR Neurology device. It focuses on establishing substantial equivalence to predicate devices and does not contain detailed information about specific acceptance criteria or a dedicated study proving performance against such criteria. The document claims substantial equivalence based on similar intended use and technical characteristics, and lists standards followed, but does not provide a table of acceptance criteria vs. device performance or details of a performance study in the manner requested.
Therefore, many of the requested sections cannot be filled from the provided text.
Here's a breakdown of what can and cannot be extracted:
1. A table of acceptance criteria and the reported device performance
- Cannot be provided. The document does not specify quantitative acceptance criteria or provide a table of device performance metrics against such criteria. It states that the device "does not introduce any new issues of safety or effectiveness" and that "risk management is ensured via a risk analysis in compliance with ISO 14971:2007."
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Cannot be provided. No information regarding a test set or data provenance is present in the document.
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)
- Cannot be provided. No information about ground truth establishment or experts is present.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Cannot be provided. No information about adjudication methods is present.
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
- Cannot be provided. The document does not describe an MRMC study or any comparison of human readers with vs. without AI assistance. The device is described as "post-processing software" and "evaluation tools" for physicians, but not explicitly as an AI-assisted diagnostic tool in the sense of a comparative effectiveness study.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Cannot be provided. No information about a standalone performance study is present.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- Cannot be provided. No information about ground truth is present.
8. The sample size for the training set
- Cannot be provided. No information about a training set for algorithm development is present.
9. How the ground truth for the training set was established
- Cannot be provided. No information about training set ground truth establishment is present.
§ 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).