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syngo DynaPBV Body is an extended software application to the InSpace 3D software option which allows the reconstruction of two-dimensional images acquired with a standard angiographic C-arm device into a three-dimensional image format.

syngo DynaPBV Body is intended for imaging primarily soft tissue for diagnosis, surgical planning, interventional procedures and treatment follow-up. It is design for the visualization of contrast enhanced blood distribution in the body using color coded relative values for diagnosis.

This software is designed to visually assist physicians in the diagnosis and treatment of vessel malformations (i.e. Aneurysms, AVM's and Stenoses)

The syngo DynaPBV Body software is an optional extension to the Inspace 3D application originally cleared under Premarket Notification K011447 on 08/03/2001. It is also similar to the cleared syngo Neuro PBV IR (K111052, May 20, 2011) which was designed for the visualization of contrast enhanced blood distribution in the arterial and venous vessels in the head.

Similar to syngo Neuro PBV IR the synqo DynaPBV Body is an add-on software option used for the visualization of contrast enhanced blood distribution in the body (e.g. thorax and abdomen) using color coded relative values for diagnosis.

This software modification does not affect the intended use of the device nor does it alter its fundamental scientific technology.

The provided text is a 510(k) summary for the syngo DynaPBV Body software. It primarily focuses on demonstrating substantial equivalence to predicate devices rather than presenting detailed standalone performance studies with specific acceptance criteria or quantitative analysis of device performance.

Therefore, many of the requested sections about acceptance criteria, detailed study design, sample sizes for test sets, ground truth establishment, expert qualifications, adjudication methods, and MRMC studies, cannot be found or fully addressed based on the provided document.

However, I can extract the information that is present and indicate where information is missing.

Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria (e.g., specific sensitivity, specificity, accuracy thresholds) for the syngo DynaPBV Body. Instead, the submission relies on demonstrating substantial equivalence to previously cleared devices. The "reported device performance" is implicitly that it performs comparably to the predicate devices.

1. A table of acceptance criteria and the reported device performance
As noted above, no quantitative acceptance criteria or corresponding reported performance metrics are provided in this 510(k) summary. The summary focuses on showing substantial equivalence in functionality and intended use to predicate devices, rather than a standalone performance study with specific benchmarks.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
Not provided in this document. No specific "test set" for performance evaluation is mentioned. The submission describes the device as an "add-on software option" and "software extension" that uses the "same post processing software, user interface, archiving and communication as the predicate InSpace 3D."

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)
Not provided. Since no specific test set or performance evaluation study is described, there is no mention of experts establishing ground truth for such a set.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not provided, as no specific test set or performance evaluation study is described.

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
No MRMC comparative effectiveness study is mentioned. The device is a post-processing software for visualization, intended to assist physicians, but its comparative effectiveness with or without AI assistance is not quantified in this submission.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This type of standalone algorithm performance study is not described in the document. The device is presented as a software tool for "visualization of contrast enhanced blood distribution... using color coded relative values for diagnosis" to "visually assist physicians." The document emphasizes its integration with existing systems and similar functionality to predicate devices.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
Not applicable, as no dedicated performance study with a defined ground truth is described in the provided summary.

8. The sample size for the training set
Not provided. A training set would be relevant for a machine learning or AI-driven algorithm. While the device processes images for visualization, this document does not indicate that it uses a training set in the context of machine learning, nor does it specify any data used to "train" the software in any other sense.

9. How the ground truth for the training set was established
Not applicable, as no training set is mentioned or described.

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