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The SIGNA(TM) Premier system is a whole body magnetic resonance scanner 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 and/or functions of the entire body, including, but not limited to, head, neck, TMJ, 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 SIGNA(TM) Premier system reflect the spatial distribution or molecular environment of nuclei exhibiting magnetic resonance. These images and/or spectra when interpreted by a trained physician vield information that may assist in diagnosis.

SIGNAT™ Premier is a whole body magnetic resonance scanner designed to support high resolution, high signal-to-noise ratio, and short scan times, and is designed for improved patient comfort and workflow. The system features a 3.0T superconducting magnet with a 70cm bore size and can image in the sagittal, coronal, axial, oblique, and double oblique planes, using various pulse sequences, imaging techniques and reconstruction algorithms. The system is designed to conform to NEMA DICOM standards (Digital Imaging and Communications in Medicine).

The provided text is related to the FDA's 510(k) premarket notification for the GE Medical Systems, LLC (GE Healthcare) SIGNA™ Premier device, a Magnetic Resonance Diagnostic Device.

The submission focuses on establishing substantial equivalence to a predicate device (SIGNA™ Architect, K163331) rather than conducting a de novo study with strict acceptance criteria and a detailed study proving the device meets them. Therefore, many of the requested elements (like specific numerical acceptance criteria, comprehensive device performance against these, ground truth establishment for training/test sets, MRMC studies, effect sizes, etc.) are not explicitly stated or detailed in this 510(k) summary.

The primary method to demonstrate equivalence here is through non-clinical testing (bench testing, compliance with standards) and sample clinical images to show acceptable diagnostic image performance.

Here's an attempt to answer the questions based on the provided text, highlighting what is present and what is absent:

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

The document does not provide a specific table of numerical acceptance criteria for image quality parameters. Instead, it states the overall conclusion regarding performance:

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

The document mentions "Sample clinical images have been included in this submission" but does not specify the sample size for this clinical image test set. It also does not provide information on the data provenance (e.g., country of origin, retrospective or prospective status).

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)

The document does not describe the establishment of a formal "ground truth" for the sample clinical images. It states, "These images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis." However, it does not specify the number of experts or their qualifications who evaluated the "sample clinical images" to determine their diagnostic acceptability or equivalence.

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

The document does not specify any adjudication method for the clinical image evaluation.

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 mentioned or performed. This device is a diagnostic imaging system (MRI scanner), not an AI-assisted diagnostic tool for interpretation. Therefore, the concept of "human readers improve with AI vs without AI assistance" is not directly applicable to this submission.

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

This question is not applicable. The SIGNA™ Premier is an MRI scanner, a hardware device that produces images. It is not an algorithm for image interpretation that would have standalone performance. Its performance relates to the quality of the images it generates, which are then interpreted by a human physician.

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

The document does not describe the use of formal ground truth (e.g., expert consensus, pathology, or outcomes data) for evaluating the sample clinical images. The evaluation appears to be based on the general diagnostic acceptability of the images by "trained physician[s]" as per FDA guidance for MR diagnostic devices.

8. The sample size for the training set

The document does not mention or describe a training set. As a hardware device (MRI scanner) rather than an AI/ML algorithm, a "training set" in the traditional sense is not applicable for its performance evaluation for regulatory submission.

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

Not applicable, as no training set is described.

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