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The software comprising the syngo.MR post-processing applications are post-processing software / applications to be used for viewing and evaluating the designated images provided by a magnetic resonance diagnostic device. All of the software applications comprising the syngo.MR post-processing applications have their own indications for use.

syngo.MR Neurology is a syngo based post-processing software for viewing, manipulating, and evaluating MR neurological images.

syngo.MR Oncology is a syngo based post-processing software for viewing, manipulating, and evaluating MR oncological images.

syngo.MR Neurology and syngo.MR Oncology are syngo.via-based post-processing software / applications to be used for viewing and evaluating MR images provided by a magnetic resonance diagnostic device and enabling structured evaluation of MR images.

syngo.MR Neurology and syngo.MR Oncology comprise of the following:

syngo.MR Neurology covers single and engine applications:
• syngo.MR Neuro Perfusion
• syngo.MR Neuro Perfusion Mismatch
• syngo.MR Neuro fMRI
• syngo.MR Tractography
• syngo.MR Neuro Perfusion Engine
• syngo.MR Neuro Perfusion Engine Pro (NEW)
• syngo.MR Neuro 3D Engine
• syngo.MR Neuro Dynamics (NEW)

syngo.MR Oncology covers single and engine applications:
syngo.MR Onco
syngo.MR 3D Lesion Segmentation
syngo.MR Tissue4D
syngo.MR Onco Engine
syngo.MR Onco Engine Pro (NEW)
syngo.MR OncoCare (NEW)

The provided text is a 510(k) summary for the syngo.MR Neurology and syngo.MR Oncology post-processing software. This document primarily focuses on establishing substantial equivalence to a predicate device rather than detailing specific acceptance criteria and a standalone study proving the device meets those criteria.

Therefore, much of the requested information regarding specific acceptance criteria, detailed study results, sample sizes, ground truth establishment for a test set, and multi-reader multi-case studies is not present in this document.

However, based on the provided text, here's what can be extracted and what information is missing:

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

This document does not provide specific quantitative acceptance criteria or a table of reported device performance metrics like sensitivity, specificity, or accuracy. The clearance is based on substantial equivalence to a predicate device (syngo.MR Post-Processing Software Version SMRVA16B, K133401) under the regulation for Picture Archiving and Communication Systems (PACS) (21 CFR 892.2050), which typically involves functional equivalence and safety rather than a diagnostic performance study against a clinical gold standard for the post-processing applications themselves.

2. Sample size 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 document refers to "non-clinical data" suggesting an equivalent safety and performance profile, but it does not detail a specific test set, its size, or provenance for a clinical performance study.

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 in the document. Since no specific clinical performance study is detailed, there's no mention of experts or their qualifications for establishing ground truth.

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

This information is not provided in the document.

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 in the document. The document focuses on the capabilities of the post-processing software for viewing, manipulating, and evaluating images, and it does not describe a comparative effectiveness study involving human readers with and without AI assistance for improved diagnostic accuracy.

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

A standalone performance study against a clinical ground truth for diagnostic parameters (like sensitivity/specificity) is not detailed in this document. The description of the device is as "post-processing software / applications to be used for viewing and evaluating," implying human interpretation of the processed images.

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

This information is not provided in the document.

8. The sample size for the training set

Given that this is a 510(k) for a PACS-related software and not a de novo AI diagnostic device, there is no mention of a training set in the context of machine learning model development. The "syngo.MR Neurology" and "syngo.MR Oncology" applications and their sub-components are described as post-processing software that perform tasks like visualization of temporal variations, calculation of differences between lesions, workflow-oriented visualization of fMRI, and 3D tractographic data utilization. While these involve algorithms, the document doesn't frame them as AI models requiring a training set in the typical sense for a diagnostic claim.

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

Since there is no mention of a training set or machine learning model development, this information is not applicable/provided in the document.

Summary of what the document indicates regarding "acceptance criteria" and "study":

The "acceptance criteria" for this 510(k) appear to be based on the general safety and effectiveness of the device, primarily by demonstrating substantial equivalence to an already cleared predicate device (syngo.MR Post-Processing Software Version SMRVA16B, K133401). The "study" that proves the device meets these criteria is an internal assessment against recognized standards and the predicate device.

• General Safety and Effectiveness Concerns: The document states that the device labeling contains instructions for use and warnings, and that risk management is ensured via a Risk Analysis compliant with ISO 14971:2007. Risks are controlled via measures in software development, SW testing, and product labeling.
• Conformance to Standards: The device conforms to applicable FDA recognized and international IEC, ISO, and NEMA standards.
• Substantial Equivalence: The primary "proof" is the comparison to the predicate device. The new functionalities (syngo.MR Neuro Dynamics and syngo.MR OncoCare) are stated to give the device "greater capabilities than the predicate" but that "the Intended Use, the basic technological characteristics and functionalities remain the same."
• Conclusion: Siemens believes the device "do[es] not raise new questions of safety or effectiveness and are substantially equivalent to the currently marketed device."

In essence, this 510(k) submission relies on the established safety and performance profile of a predicate device and adherence to general device safety and quality standards, rather than presenting a de novo clinical performance study with defined acceptance criteria for diagnostic accuracy.

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The software comprising the syngo.MR post-processing applications are postprocessing software / applications to be used for viewing and evaluating the designated images provided by a magnetic resonance diagnostic device. All of the software applications comprising the syngo.MR post-processing applications have their own indications for use.

syngo.mMR General is a syngo based post-processing software for viewing, manipulating, and evaluating MR, PET, and CT images as well as MR-PET and CT-PET images.

syngo.mMR General is a syngo.via-based post-processing software / application to be used for viewing and evaluating MR images provided by a magnetic resonance diagnostic device and enabling structured evaluation of MR images.

The provided text is a 510(k) summary for syngo.mMR General, a post-processing software. A 510(k) submission generally aims to demonstrate substantial equivalence to a legally marketed predicate device, rather than proving a device meets specific, pre-defined acceptance criteria through a clinical study with reported performance metrics like sensitivity and specificity.

Therefore, the document does not provide the specific information you are asking for regarding acceptance criteria, a study proving the device meets those criteria, or details such as sample sizes for test/training sets, expert qualifications, or adjudication methods in the context of a performance study demonstrating diagnostic accuracy.

The document states:

• "Siemens is of the opinion that the syngo.MR post-processing application does not raise new questions of safety or effectiveness and are substantially equivalent to the currently marketed device syngo.MR Post-Processing Software Version SMRVA16B (K133401 cleared on March 11, 2014)" {3}.
• "The syngo.MR post-processing application is intended for similar indications as cleared in the predicate device." {3}.
• "There are minor changes to the indications for use for the subject device with regards to syngo.mMR General. The differences give the device greater capabilities than the predicate, but the technological characteristics and functionalities are similar." {3}.

This type of submission relies on demonstrating that the new device has "substantially equivalent" technological characteristics and indications for use to a previously cleared device, not on presenting novel performance data against specific acceptance criteria.

Therefore, I cannot populate the table or answer most of your questions as the information is not present in the provided text. The document focuses on showing substantial equivalence to a predicate device, which is a different regulatory pathway than providing a performance study against specific acceptance criteria.

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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.

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