Search Results

Your MAGNETOM system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces transverse, sagittal, coronal and oblique cross sectional images, spectroscopic images and/or spectra, and that displays the internal structure and/or function of the head, body, or extremities. Other physical parameters derived from the images and/or spectra may also be produced. Depending on the region of interest, contrast agents may be used. These images and or spectra and the physical parameters derived from the images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis.

Your MAGNETOM system may also be used for imaging during interventional procedures when performed with MR compatible devices such as in-room displays and MR Safe biopsy needles.

MAGNETOM Vida, MAGNETOM Sola, MAGNETOM Lumina, MAGNETOM Altea with software syngo MR XA31A includes new and modified hardware and software compared to the predicate device, MAGNETOM Vida with software syngo MR XA20A.

This document describes the Siemens MAGNETOM MR system (various models) with syngo MR XA31A software, and it does not describe an AI device. The information provided is a 510(k) summary for a Magnetic Resonance Diagnostic Device (MRDD). The "Deep Resolve Sharp" and "Deep Resolve Gain" features are mentioned as using "trained convolutional neuronal networks" but the document does not provide details on acceptance criteria or studies specific to the AI components as requested.

Therefore, many of the requested items (e.g., sample sizes for training/test sets for AI, expert consensus for ground truth, MRMC studies) cannot be extracted from this document because it is primarily focused on the substantial equivalence of the overall MR system and its general technological characteristics, not a specific AI algorithm requiring detailed performance studies against a clinical ground truth.

However, I can extract the available information, especially concerning the "Deep Resolve Sharp" and "Deep Resolve Gain" features, and note where the requested information is not present.

Here's the breakdown of available information, with specific answers to your questions where possible:

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

The document does not specify quantitative acceptance criteria for the "Deep Resolve Sharp" or "Deep Resolve Gain" features, nor does it present a table of reported device performance metrics for these features in the context of clinical accuracy or diagnostic improvement specifically. The performance testing mentioned is general for the entire system ("Image quality assessments," "Performance bench test," "Software verification and validation"), concluding that devices "perform as intended and are thus substantially equivalent."

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

• Test Set Sample Size: Not explicitly stated for specific features like "Deep Resolve Sharp" or "Deep Resolve Gain." The document broadly mentions "Sample clinical images" were used for "Image quality assessments."
• Data Provenance (Country/Retrospective/Prospective): Not specified in the document.

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)

Not specified. The document states "Image quality assessments by sample clinical images" and that the "images...when interpreted by a trained physician yield information that may assist in diagnosis," but it does not detail the number or qualifications of experts involved in these assessments for specific software features or for establishing ground truth for any AI component.

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

Not specified.

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

An MRMC study was not described for the "Deep Resolve Sharp" or "Deep Resolve Gain" features or any other AI component. The document references clinical publications for some features (e.g., Prostate Dot Engine, GRE_WAVE, SVS_EDIT) but these are general publications related to the underlying clinical concepts or techniques, not comparative effectiveness studies of the system's AI features versus human performance. The statement "No additional clinical tests were conducted to support substantial equivalence for the subject devices" reinforces this.

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

While "Deep Resolve Sharp" and "Deep Resolve Gain" involve "trained convolutional neuronal networks," the document does not describe standalone performance studies for these algorithms. Their inclusion is framed as an enhancement to the overall MR system's image processing capabilities, rather than a separate diagnostic AI tool. The stated purpose of Deep Resolve Sharp is to "increases the perceived sharpness of the interpolated images" and Deep Resolve Gain "improves the SNR of the scanned images," both being image reconstruction/enhancement features.

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

Not specified for any AI-related features. For general image quality assessment, the "trained physician" is mentioned as interpreting images to assist in diagnosis, implying clinical interpretation, but no formal ground truth establishment process is detailed.

8. The sample size for the training set

Not specified for the "trained convolutional neuronal networks" used in "Deep Resolve Sharp" or "Deep Resolve Gain."

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

Not specified.

Ask a specific question about this device