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uWS-MR is a software solution intended to be used for viewing, manipulation, and storage of medical images. It supports interpretation and evaluations within healthcare institutions. It has the following additional indications:

The MR Stitching is intended to create full-format images from overlapping MR volume data sets acquired at multiple stages.

The Dynamic application is intended to provide a general post-processing tool for time course studies.

The Image Fusion application is intended to combine two different image series so that the displayed anatomical structures match in both series.

MRS (MR Spectroscopy) is intended to evaluate the molecule constitution and spatial distribution of cell metabolism. It provides a set of tools to view, process, and analyze the complex MRS data. This application supports the analysis for both SVS (Single Voxel Spectroscopy) and CSI (Chemical Shift Imaging) data.

The MAPs application is intended to provide a number of arithmetic and statistical functions for evaluating dynamic processes and images. These functions are applied to the grayscale values of medical images.

The MR Breast Evaluation application provides the user a tool to calculate parameter maps from contrast-enhanced timecourse images.

The Brain Perfusion application is intended to allow the visualizations in the dynamic susceptibility time series of MR datasets.

MR Vessel Analysis is intended to provide a tool for viewing, manipulating MR vascular images. The Inner view application is intended to perform a virtual camera view through hollow structures (cavities), such as vessels.

uWS-MR is a comprehensive software solution designed to process, review and analyze MR (Magnetic Resonance Imaging) studies. It can transfer images in DICOM 3.0 format over a medical imaging network or import images from external storage devices such as CD/DVDs or flash drives. These images can be functional data, as well as anatomical datasets. It can be at one or more time-points or include one or more time-frames. Multiple display formats including MIP and volume rendering and multiple statistical analysis including mean, maximum and minimum over a user-defined region is supported. A trained, licensed physician can interpret these displayed images as well as the statistics as per standard practice.

The provided document is a 510(k) premarket notification for the uWS-MR software. It focuses on demonstrating substantial equivalence to predicate devices, rather than presenting a standalone study with detailed acceptance criteria and performance metrics for a novel AI-powered diagnostic device.

Therefore, much of the requested information regarding specific acceptance criteria, detailed study design for proving the device meets those criteria, sample sizes for test and training sets, expert qualifications, and ground truth establishment cannot be fully extracted from this document.

Here's what can be inferred and stated based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly list quantitative acceptance criteria in a pass/fail format nor provide specific, measurable performance metrics for the proposed device (uWS-MR) in comparison to such criteria. Instead, it relies on demonstrating substantial equivalence to predicate devices by comparing their features and functionalities. The "Remark" column consistently states "Same," indicating that the proposed device's features align with its predicates, implying it meets comparable performance.

2. Sample Size Used for the Test Set and the Data Provenance

The document states: "Software verification and validation testing was provided to demonstrate safety and efficacy of the proposed device." and lists "Performance Verification" for various applications. However, it does not specify the sample size used for these performance tests (test set) or the data provenance (e.g., country of origin, retrospective/prospective nature).

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts

This information is not provided in the document. The filing focuses on technical and functional equivalence, not on clinical performance evaluated against expert ground truth.

4. Adjudication Method

This information is not provided in the document.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

A multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned as being performed or required. The submission is a 510(k) for substantial equivalence, which often relies more on technical verification and comparison to predicate devices rather than new clinical effectiveness studies.

6. Standalone Performance

The document clearly states: "Not Applicable to the proposed device, because the device is stand-alone software." This implies that the device is intended to perform its functions (viewing, manipulation, post-processing) as a standalone software, and its performance was evaluated in this standalone context during verification and validation, aligning with the predicates which are also software solutions. However, no specific standalone performance metrics are provided.

7. Type of Ground Truth Used

The document does not explicitly state the "type of ground truth" used for performance verification. Given the nature of the device (image post-processing software) and the 510(k) pathway, performance verification likely involved:

• Technical validation: Comparing outputs of uWS-MR's features (e.g., image stitching, parameter maps, ROI measurements) against known good results, simulated data, or outputs from the predicate devices.
• Functional testing: Ensuring features operate as intended (e.g., if a rotation function rotates the image correctly).
  Pathology or outcomes data are typically used for diagnostic devices with novel clinical claims, which is not the primary focus here.

8. Sample Size for the Training Set

The concept of a "training set" typically applies to machine learning or AI models that learn from data. While the uWS-MR is post-processing software, and could potentially incorporate AI elements (though not explicitly stated beyond general "post-processing"), the document does not mention a training set size. This strongly suggests that a machine learning or deep learning model with a distinct training phase, as commonly understood, was not a primary component evaluated in this filing or, if present, its training data details were not part of this summary.

9. How the Ground Truth for the Training Set Was Established

As no training set is mentioned (see point 8), the establishment of its ground truth is not applicable and therefore not described in the document.

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

syngo.MR BreVis is a software package intended for use in viewing and analyzing magnetic resonance imaging (MRI) studies. syngo.MR BreVis supports evaluation of dynamic MR data. Depending on the region of interest, contrast agents may or may not be used.

syngo.MR BreVis automatically registers serial patient motion to minimize the impact of patient motion and visualizes different enhancement characteristics in those areas that are within the scope of the indications for use of MRI FDA approved contrast agents (parametric image maps). Furthermore, it performs other user-defined postprocessing functions such as image subtractions, multiplanar reformats and maximum intensity projections. The resulting information can be displayed in a variety of formats. including a parametric image overlaid onto the source image, syngo.MR BreVis can also be used to provide measurements of the diameters, areas and volumes.

Furthermore syngo.MR BreVis can evaluate the uptake characteristics of segmented tissues that are within the scope of the indications for use of MRI FDA approved contrast agents. syngo.MR BreVis is optimized for viewing breast MR studies, and it also displays images from a number of other imaging modalities, like digitized mammographic images.

The images by other imaging modalities displayed with syngo.MR BreVis must not be used for primary diagnostic interpretation. syngo.MR BreVis also includes the option to add annotations based on the American College of Radiology's BI-RADS (Breast Imaging Reporting and Data System). When interpreted by a skilled physician, syngo.MR BreVis provides information that may be useful in diagnosis. Patient management decisions should not be made based solely on the results of syngo.MR BreVis analysis.

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

The provided text is a 510(k) summary for the syngo.MR post-processing software (Version SMRVA16B). This type of submission focuses on demonstrating "substantial equivalence" to a legally marketed predicate device rather than presenting an extensive study proving performance against specific acceptance criteria.

Therefore, the document does not contain the detailed information required to fully answer all points of your request, such as specific acceptance criteria for a new clinical study, the reported device performance against those criteria, sample sizes for test sets, number/qualifications of experts for ground truth, adjudication methods, MRMC studies, standalone performance details, training set information, or how ground truth for training was established.

Instead, the submission argues that the new device has "same technological characteristics and functionalities as the predicate device, and do not introduce any new issues of safety or effectiveness." The "study" here is essentially a comparison to the predicate device.

However, I can extract the following based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance:

This information is not provided in the document as a new clinical study with specific acceptance criteria was not conducted for this 510(k) submission. The entire submission is built on the premise of substantial equivalence to a predicate device.

2. Sample size used for the test set and the data provenance:

• Sample Size for Test Set: Not applicable. No new clinical test set was used to establish performance against new acceptance criteria. The submission relies on the predicate device's clearance.
• Data Provenance: Not applicable.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable. Ground truth for a new test set was not established for this submission.

4. Adjudication method for the test set:

• Not applicable.

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 was done for this submission. The device is a post-processing software, not specifically an AI-driven diagnostic aid in the context of an MRMC study to compare reader performance with and without AI assistance.

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

• Not explicitly stated as a standalone performance study with specific metrics. The software's functionality is described as "post-processing software for viewing, manipulating, and evaluating" images, implying it's a tool for human readers rather than making independent diagnostic decisions. The document states, "Patient management decisions should not be made based solely on the results of syngo.MR BreVis analysis."

7. The type of ground truth used:

• Not applicable for a new study. The safety and effectiveness are based on demonstrating substantial equivalence to a previously cleared device. For the predicate device, the regulatory clearance would have been based on appropriate clinical data and ground truth (which could be expert consensus, pathology, or outcomes data depending on the device).

8. The sample size for the training set:

• Not applicable. This document describes a 510(k) submission for new versions/modules of existing software, relying on substantial equivalence, not a de novo clearance requiring a new AI model with a training set.

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

• Not applicable.

Explanation of the "Study" and "Acceptance Criteria" in this Context:

In a 510(k) submission based on "substantial equivalence," the "study" is less about generating new performance data against novel acceptance criteria and more about demonstrating that the new device is as safe and effective as a legally marketed predicate device.

The "acceptance criteria" here implicitly refer to the regulatory standards and performance of the predicate device, K130749 (syngo.MR Post-Processing Software Version SMRVA16A), which was cleared on August 20, 2013. The manufacturer's claim is that the updated software "do not raise new questions of safety or effectiveness" and has "same technological characteristics and functionalities as the predicate device" despite having "greater capabilities."

Therefore, the "proof" the device meets acceptance criteria is the argument that it is substantially equivalent to a device that has already met the FDA's safety and effectiveness requirements.

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