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The EMMA 1.5T MR System is a magnetic resonance diagnostic device (MRDD) which produces transverse, sagittal, coronal and oblique cross-sectional images, and those display the internal structure and/or function of the head, body, or extremities. Depending on the region of interest, contrast agents may be used. These images when interpreted by trained physician yield information that may assist medical diagnosis.

The EMMA 1.5T MRI System is a 1.5T superconducting magnet MRI system which produces transverse, sagittal, coronal and oblique cross-sectional images, and those display the internal structure and/or function of the head, body, or extremities. It is composed of Magnet, Magnet Enclosure, Patient Table, Gradient Coil, Transmission Coil, Receiver Coil, Client PC, and Imaging Cabinet. The system software, Prodiva, a Windows-based software, is an interactive program with user friendly interface. The device is conformed to IEC and DICOM standards.

The provided text is a 510(k) summary for the EMMA 1.5T MRI System. It details the device's characteristics and its substantial equivalence to a predicate device, focusing on non-clinical performance data and a comparison of clinical images.

Here's an analysis based on your request, highlighting the information available in the text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the EMMA 1.5T MRI System are largely based on compliance with various NEMA MS and IEC standards, demonstrating that its performance is equivalent to the predicate device. The text does not provide specific numerical acceptance criteria alongside numerical reported performance for each metric; instead, it states compliance with the standards.

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

The document mentions "Sample clinical images are provided to verify the claim of filing device's capability in generating images for diagnostic purposes." and "Sample clinical image sets from filing device and predicate device on same pulse sequences are provided to demonstrate the substantial equivalence."

• Sample size used for the test set: Not explicitly stated. The term "sample clinical images" suggests a limited set, but no number is given.
• Data provenance: Not explicitly stated. It is implied these are clinical images, but information on country of origin or whether they are retrospective or prospective is not provided.

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

This information is not provided in the document. The text states that the resulting images, "when interpreted by trained physician yield information that may assist medical diagnosis," but it does not detail an expert review process for a specific test set or the qualifications of any such experts.

4. Adjudication Method for the Test Set

This information is not provided in the document. There is no mention of an adjudication method like 2+1 or 3+1 for establishing ground truth on a test set.

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

A MRMC comparative effectiveness study is not mentioned in the document. The study performed focuses on demonstrating substantial equivalence through non-clinical performance and a visual comparison of sample clinical images, not on quantifying human reader improvement with or without AI assistance.

6. Standalone (Algorithm Only) Performance Study

This is an MRI system, not an AI algorithm. Therefore, a standalone (algorithm only) performance study in the context of AI is not applicable/not performed as described for diagnostic algorithms. The performance studies are for the imaging system itself. The document mentions "Software Verification and Validation Testing," which is focused on the software's functionality and safety within the device, not its standalone diagnostic performance.

7. Type of Ground Truth Used

For the clinical image comparison, the ground truth is implicitly based on the visual interpretability of the images by a "trained physician" for diagnostic assistance. However, a formal "ground truth" (e.g., pathology, clinical follow-up) for a specific diagnostic outcome for these sample images is not explicitly stated or detailed. The comparison is about the quality and diagnostic utility of the images produced by the new device versus the predicate device.

8. Sample Size for the Training Set

This information is not applicable as the document describes an MRI system, not an AI algorithm that requires a training set in that conventional sense. The "software" in this context refers to the operating and image reconstruction software, which is traditionally developed and validated through engineering processes, not trained on radiological data like an AI model.

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

This information is not applicable for the same reason as point 8.

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