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The 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.

The 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 and MAGNETOM Sola with Nexaris Angio-MR include modified hardware compared to the predicate device, MAGNETOM Vida with software syngo MR XA31A (K203443). A high-level summary of the modified hardware is provided below:

Hardware

Modified Hardware

• The Nexaris Dockable Table is a variant of the MR patient table which is used for intraoperative or interventional imaging. It enables the patient transfer between OR/ARTIS tables and the MR system without repositioning on the MR patient table and vice versa during interventional procedures and surgeries. Additionally, it can be used for diagnostic imaging.

The provided text is a 510(k) Summary for a medical device (MAGNETOM Vida and MAGNETOM Sola with Nexaris Angio-MR). This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving the device meets specific performance acceptance criteria through clinical studies for novel claims.

Therefore, many of the requested details about acceptance criteria, specific performance metrics, sample sizes for test sets, expert ground truth establishment, MRMC studies, or standalone algorithm performance are not directly available in this document. The submission relies on demonstrating that the modified hardware of the new device maintains the safety and performance profile of the predicate device.

Here's an analysis based on the information provided, highlighting what is present and what is absent:

1. Table of Acceptance Criteria and Reported Device Performance

This document does not provide a table with specific acceptance criteria (e.g., sensitivity, specificity, accuracy targets) and corresponding reported device performance metrics for a novel diagnostic claim. Instead, the "acceptance criteria" are implied by compliance with recognized standards and successful verification and validation of modified hardware, demonstrating equivalent safety and performance to the predicate device.

The reported "performance" is that the device "perform[s] as intended" and "bear[s] an equivalent safety and performance profile to that of the predicate device."

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

• Sample Size: Not applicable/not provided. The submission focuses on hardware modifications and compliance with standards, not on a clinical test set for diagnostic performance.
• Data Provenance: Not applicable/not provided for a clinical test set. The data provenance described is related to non-clinical performance testing of modified hardware against engineering and safety standards.

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

Not applicable. As there was no clinical diagnostic test set evaluated for novel claims, there was no need for experts to establish ground truth in this context. The "truth" evaluated was compliance with engineering and safety standards.

4. Adjudication Method (e.g., 2+1, 3+1, none) for the Test Set

Not applicable. No adjudications were performed related to a diagnostic test set.

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 done, as this submission is for a Magnetic Resonance Diagnostic Device (MRDD) and not an AI-assisted diagnostic tool or software. The document explicitly states: "No additional clinical tests were conducted to support substantial equivalence for the subject devices."

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

No standalone performance study of an algorithm was done. This submission is for an MRDD system with modified hardware, not a standalone algorithm.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

The "ground truth" for the nonclinical tests was based on engineering specifications, recognized safety standards (e.g., AAMI / ANSI ES60601-1, 21 CFR §820.30), and risk management principles (ISO 14971).

8. The Sample Size for the Training Set

Not applicable. This submission does not involve an AI algorithm that would require a training set.

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

Not applicable. This submission does not involve an AI algorithm or a training set.

Summary of the Study Proving Acceptance Criteria:

The study proving the device meets the "acceptance criteria" (which in this context are interpreted as demonstrating safe and equivalent performance to the predicate device) was a series of nonclinical performance tests focused on the modified hardware.

• Study Type: Nonclinical performance testing (verification and validation against established standards and engineering requirements).
• Focus: Evaluation of "modified hardware" (Nexaris Dockable Table) to ensure it performs as intended and maintains the safety and performance profile of the predicate device.
• Tests Conducted:
  • Electrical, mechanical, structural, and related system safety tests (utilizing AAMI / ANSI ES60601-1).
  • Verification and validation (in accordance with 21 CFR §820.30).
• Conclusion: The results of these nonclinical tests demonstrated that the modified features "bear an equivalent safety and performance profile to that of the predicate device." The device also conforms to various recognized standards including IEC 62304, ISO 14971, IEC 60601-1 series, and others listed in the document.

In essence, the "study" was a comprehensive engineering and regulatory compliance assessment of the hardware changes, leveraging industry standards and internal verification processes instead of clinical performance studies with diagnostic endpoints.

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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.

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