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MAVRIC SL is a combination of an acquisition technique and post-processing software intended for use on GE 1.5T and 3.0T MR systems. MAVRIC SL is suitable for use on all patients with passive MR Conditional orthopedics implants that are scanned according to the conditions of safe use for the specific MR Conditional implant being scanned. In addition, MAVRIC SL is suitable for use on patients without implants that are cleared for MR exams.

MAVRIC SL helps reduce artifacts caused by presence of metal in both in-plane and through-plane dimensions compared to conventional MR imaging techniques. Thus MAVRIC SL allows visualizing more tissue in the vicinity of MR Conditional implanted metal instrumentation.

When interpreted by a trained physician, images generated by MAVRIC SL provide information that can be useful in determining a diagnosis.

MAVRIC SL is a software application offered as an option for GE MR scanners. MAVRIC SL is based upon conventional 2D-FSE and then adds view-angle-tilting and additional phase-encoding in the slice-selective dimension. As a result, MAVRIC SL has reduced image artifacts and provides adequate spectral coverage in the presence of metal implants.

MAVRIC SL is a combination of an acquisition technique and post-processing software intended for use on GE 1.5T and 3.0T MR systems. MAVRIC SL is suitable for use on all patients cleared for MR exams.

MAVRIC SL helps reduce artifacts caused by presence of metal in both in-plane and through-plane dimensions compared to conventional MR imaging techniques. Thus MAVRIC SL allows visualizing tissue in the vicinity of implanted metal instrumentation.

When interpreted by a trained physician, images generated by MAVRIC SL provide information that can be useful in determining a diagnosis.

MAVRIC SL is an FSE-based, 3D Multi-Spectral Imaging technique. Individually encoded spectral images in the MAVRIC SL technique overlap in the frequency domain and together span a wide spectrum of spins located near metal implants. MAVRIC SL generates a final image by combining the individually encoded spectral images in a post-processing step. MAVRIC SL software option employs the same fundamental scientific technology as its predicate device.

The provided text is a 510(k) Premarket Notification Submission for GE Healthcare's MAVRIC SL. It details the device, its intended use, and a summary of non-clinical and clinical tests. However, it does not contain the specific acceptance criteria or the detailed study results that directly prove the device meets these criteria. The document states that "The following clinical testing has been performed to validate the MAVRIC SL technique: Application validation, Clinical imaging," and concludes that "GE Healthcare considers the MAVRIC SL software option to be as safe, as effective, and performance is substantially equivalent to the predicate device." This suggests that a study was conducted, but the specifics of that study (like metrics, sample sizes, ground truth establishment, or expert details) are not included in the provided excerpt.

Therefore, many of the requested details cannot be extracted from this document.

Here's an attempt to answer the questions based on the available information, noting where information is missing:

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

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 for Test Set: Not specified in the provided text.
• Data Provenance: Not specified in the provided text.
• Retrospective/Prospective: Not specified in the provided text.

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)

• Number of Experts: Not specified. The document mentions "interpreted by a trained physician," but does not detail the experts involved in establishing ground truth for testing.
• Qualifications of Experts: Not specified.

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

• Adjudication Method: Not specified in the provided text.

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

• The document implies clinical imaging was done for validation, but it does not explicitly state that an MRMC comparative effectiveness study was performed, nor does it provide any effect size related to human readers improving with or without AI assistance. The device is a "software option for Magnetic Resonance Imaging System" and is described as "an acquisition technique and post-processing software," not an AI-assisted diagnostic tool in the sense of providing specific interpretations or aiding human readers in decision-making beyond image quality improvement.

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

• The MAVRIC SL is described as an imaging technique and post-processing software that generates images for interpretation by a physician. It is not presented as a standalone diagnostic algorithm. Its performance is intrinsically linked to the image generation and subsequent human interpretation. The text states: "When interpreted by a trained physician, images generated by MAVRIC SL provide information that can be useful in determining a diagnosis."

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

• Type of Ground Truth: Not specified in the provided text.

8. The sample size for the training set

• Sample Size for Training Set: The document does not mention a distinct "training set" in the context of an algorithm or machine learning model. MAVRIC SL is described as an "acquisition technique and post-processing software," implying physics-based image reconstruction rather than a trainable AI model in the modern sense. Therefore, this question is likely not applicable to the description provided.

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

• How Ground Truth for Training Set was Established: Not applicable, as no training set for a machine learning model is mentioned.

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