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The Discovery MR750 3.0T, Discovery MR450 1.5T, Discovery MR750w 3.0T and the Optima MR450w 1.5T systems are whole body magnetic resonance scanners designed to support high signal-to-noise ratio, and short scan times. It is indicated for use as a diagnostic imaging device to produce axial, sagittal, coronal, and oblique images, spectroscopic images, parametric maps, and/or spectra, dynamic images of the structures of the entire body, including, but not limited to, head, neck, TMI, spine, breast, heart, abdomen, pelvis, joints, prostate, blood vessels, and musculoskeletal regions of the body. Depending on the region of interest being imaged, contrast agents may be used.

The images produced by the Discovery MR750 3.0T, Discovery MR450 1.5T, Discovery MR750w 3.0T and the Optima MR450w 1.5T systems reflect the spatial distribution or molecular environment of nuclei exhibiting magnetic resonance. These images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis.

The Discovery MR750 3.0T, Discovery MR450 1.5T, Discovery MR750w 3.0T and the Optima MR450w 1.5T are whole body magnetic resonance scanners designed to support high resolution, high signal-to-noise ratio, and short scan times. The systems feature a superconducting magnet operating at 1.5 Tesla or 3.0 Telsa. The data acquisition system accommodates up to 32 independent receive channels in various increments and multiple independent coil elements per channel during a single acquisition series. The system uses a combination of time-varying magnetic fields (gradients) and RF transmissions to obtain information regarding the density and position of elements exhibiting magnetic resonance. The system can image in the sagittal, coronal, axial, oblique, and double oblique planes, using various pulse sequences and reconstruction algorithms.

This Discovery MR750 3.0T, Discovery MR450 1.5T, Discovery MR750w 3.0T, and the Optima MR450w 1.5T systems are designed to conform to NEMA DICOM standards (Digital Imaging and Communications in Medicine).

The DV25 release is introducing new software features onto these existing MR Systems.

The provided text does not contain detailed acceptance criteria or a study proving the device meets specific performance criteria in the format requested.

The document is a 510(k) Premarket Notification Submission for GE Medical Systems' Discovery MR series and Optima MR series devices. It primarily focuses on demonstrating substantial equivalence to a previously cleared predicate device (K132376) for minor changes, specifically new software features (DV25 release including DISCO, MDE Plus, and Silent Suite updates) and hardware modifications for obsolescence and operating system compatibility.

Here's what can be extracted based on the provided text, and what information is not available:

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

The document does not provide a table with specific, quantifiable acceptance criteria (e.g., sensitivity, specificity, accuracy for a diagnostic task) and corresponding reported device performance. It generally states that "The testing was completed with passing results per the pass/fail criteria defined in the test cases," and "The clinical results demonstrated that the Discovery MR750 3.0T, Discovery MR450 1.5T, Discovery MR750w 3.0T and the Optima MR450w 1.5T maintain the same imaging performance results as its predicate devices (K132376)." These are high-level statements without specific metrics or thresholds.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective):

• Sample Size: Not specified. The document mentions "Internal scans were conducted as part of validation for workflow and image quality," but does not provide a sample size (number of patients, cases, or images) for these internal scans.
• Data Provenance: The scans were "internal," suggesting they were conducted within GE Healthcare, but the country of origin, retrospective/prospective nature, or demographic details are not provided.

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. The document makes no mention of external experts or how ground truth was established for the internal validation scans. The statement "These images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis" relates to the general use of MR images, not to the ground truth establishment for a specific study.

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

This information is not provided.

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:

• MRMC Study: No, an MRMC comparative effectiveness study was not conducted or described. The document states, "The subject of this premarket submission... did not require external clinical studies to support substantial equivalence."
• AI Assistance Effect Size: The device described is a Magnetic Resonance (MR) scanner, not an AI-based diagnostic tool for interpretation. Therefore, the concept of human readers improving with AI assistance is not applicable to the information presented. The software updates are for scanner capabilities (image acquisition, image quality, workflow), not for AI-driven diagnostic interpretation.

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

The device is an MR scanner, which generates images that are then interpreted by a trained physician. It is not an algorithm that provides a standalone diagnosis. Therefore, standalone algorithm-only performance in the context of interpretation is not applicable. The "performance" being evaluated here relates to image quality and features of the MR scanner itself.

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

This information is not provided. Given the nature of an MR scanner (image acquisition), "workflow and image quality" validation might involve assessing image metrics, artifact presence, resolution, signal-to-noise ratio, and visual assessment of anatomical detail, rather than a clinical ground truth like pathology for a specific disease.

8. The sample size for the training set:

The document does not describe the development of a model that would require a "training set" in the context of machine learning or AI. The changes are software features and hardware modifications for an existing MR system. Therefore, this question is not applicable.

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

As there is no mention of a training set, this question is not applicable.

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