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The uMR Omega system is indicated for use as a magnetic device (MRDD) that produces sagittal, transverse, coronal, and oblique cross sectional images, and that display internal anatomical structure and/or function of the head, body and extremities.

These images and the physical parameters derived from the images when interpreted by a trained physician yield information that may assist the diagnosis. Contrast agents may be used depending on the region of interest of the scan.

u WS-MR is a software solution intended to be used for viewing, manipulation, and storage of medical images. It supports interpretation and evaluation of examinations 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, and evaluating MR vascular images.

The Inner view application is intended to perform a virtual camera view through hollow structures (cavities), such as vessels.

The DCE analysis is intended to view, manipulate, and evaluate dynamic contrast-enhanced MRI images.

The United Neuro is intended to view, manipulate, and evaluate MR neurological images.

The MR Cardiac Analysis application is intended to be used for viewing, post-processing and quantitative evaluation of cardiac magnetic resonance data.

The uMR Omega is a 3.0T superconducting magnetic resonance diagnostic device with a 75cm size patient bore. It consists of components such as magnet, RF power amplifier, RF coils, gradient power amplifier, gradient coils, patient table, spectrometer, computer, equipment cabinets, power distribution system, internal communication system, and vital signal module etc. The uMR Omega Magnetic Resonance Diagnostic Device is designed to conform to NEMA and DICOM standards.

uWS-MR is a comprehensive software solution designed to process, review and analyze MR (Magnetic Resonance Imaging) studies. It can be used as a stand-alone SaMD or a post processing application option for cleared UIH (Shanghai United Imaging Healthcare Co.,Ltd.) MR Scanners.

The uMR 780 is a 3.0T superconducting magnetic resonance diagnostic device with a 65cm size patient bore. It consists of components such as magnet, RF power amplifier. RF coils, gradient power amplifier, gradient coils, patient table, spectrometer, computer, equipment cabinets, power distribution system, internal communication system, and vital signal module etc. The uMR 780 Magnetic Resonance Diagnostic Device is designed to conform to NEMA and DICOM standards.

The document describes the performance testing for the "DeepRecon" feature, an artificial intelligence (AI)-assisted image processing algorithm, of the uMR Omega with uWS-MR-MRS device.

Here's a breakdown of the requested information:

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

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

• Sample Size for Test Set: 77 US subjects.
• Data Provenance: The testing data was collected from various clinical sites in the US, ensuring diverse demographic distributions covering various genders, age groups, ethnicities, and BMI groups. The data was collected during separated time periods and on subjects different from the training data, making it completely independent and having no overlap with the training data.

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

• Number of Experts: Not explicitly stated, but the document mentions "American Board of Radiologists certificated physicians" evaluated the DeepRecon images. This implies a group of such experts.
• Qualifications of Experts: American Board of Radiologists certificated physicians.

4. Adjudication method for the test set

• The document states that "All DeepRecon images were rated with equivalent or higher scores in terms of diagnosis quality" by the radiologists. This suggests a consensus or rating process, but the specific adjudication method (e.g., majority vote, sequential review) is not detailed.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and if so, what was the effect size of how much human readers improve with AI vs without AI assistance

• The document implies a human-in-the-loop evaluation as "DeepRecon images were evaluated by American Board of Radiologists certificated physicians," and they "verified that DeepRecon meets the requirements of clinical diagnosis." It also states "All DeepRecon images were rated with equivalent or higher scores in terms of diagnosis quality." However, this is not explicitly described as a formal MRMC comparative effectiveness study designed to quantify human reader improvement with vs. without AI assistance. The focus seems to be on the diagnostic quality of the DeepRecon images themselves. No specific effect size is provided for human reader improvement with AI assistance.

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

• Yes, a standalone (algorithm only) performance evaluation was conducted based on objective metrics like Image SNR, Image Uniformity, Image Contrast, and Structure Measurements, as detailed in Table b. The radiologist evaluation appears to be a subsequent step to confirm clinical utility.

7. The type of ground truth used

• For the objective performance metrics (SNR, uniformity, contrast, structure measurements), the ground truth for comparison appears to be the images "without DeepRecon (NADR)".
• For the expert evaluation, the ground truth is implicitly based on the expert consensus of the American Board of Radiologists certificated physicians regarding the diagnostic quality of the images.
• For the training data ground truth (see point 9), it was established using "multiple-averaged images with high-resolution and high SNR."

8. The sample size for the training set

• The training data for DeepRecon was collected from 264 volunteers.

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

• The ground truth for the training dataset was established by collecting "multiple-averaged images with high-resolution and high SNR" from each subject. The input images for training were then generated by sequentially reducing the SNR and resolution of these high-quality ground-truth images. All data used for training underwent manual quality control.

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