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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 Amira and MAGNETOM Sempra with syngo MR XA50M include new and modified features comparing to the predicate devices MAGNETOM Amira and MAGNETOM Sempra with syngo MR XA12M (K183221, cleared on February 14, 2019).

The provided document is a 510(k) summary for the Siemens MAGNETOM Amira and Sempra MR systems, detailing their substantial equivalence to predicate devices. It describes new and modified hardware and software features, including AI-powered "Deep Resolve Boost" and "Deep Resolve Sharp."

However, the document does not contain the detailed information necessary to fully answer the specific questions about acceptance criteria and a study proving the device meets those criteria, particularly in the context of AI performance. The provided text is a summary for regulatory clearance, not a clinical study report.

Specifically, it lacks:

• Concrete, quantifiable acceptance criteria for the AI features (e.g., a specific PSNR threshold that defines "acceptance").
• A comparative effectiveness study (MRMC) to show human reader improvement with AI assistance.
• Stand-alone algorithm performance metrics for the AI features (beyond general quality metrics like PSNR/SSIM, which are not explicitly presented as acceptance criteria).
• Details on expert involvement, adjudication, or ground truth establishment for a test set used for regulatory acceptance, as the "test statistics and test results" section refers to quality metrics and visual inspection, and "clinical settings with cooperation partners" rather than a formal test set for regulatory submission.

The "Test statistics and test results" section for Deep Resolve Boost mentions "After successful passing of the quality metrics tests, work-in-progress packages of the network were delivered and evaluated in clinical settings with cooperation partners." It also mentions "seven peer-reviewed publications" covering 427 patients which "concluded that the work-in-progress package and the reconstruction algorithm can be beneficially used for clinical routine imaging." This indicates real-world evaluation but does not provide specific acceptance criteria or detailed study results for the regulatory submission itself.

Based on the provided text, here's what can be extracted and what is missing:

1. Table of acceptance criteria and reported device performance:

The document does not explicitly state quantifiable "acceptance criteria" for the AI features (Deep Resolve Boost and Deep Resolve Sharp) that were used for regulatory submission. Instead, it describes general successful evaluation methods:

• Successful passing of quality metrics tests (PSNR, SSIM)
• Visual inspection to detect potential artifacts
• Evaluation in clinical settings with cooperation partners
• No misinterpretation, alteration, suppression, or introduction of anatomical information reported | Deep Resolve Boost:
• Impact characterized by PSNR and SSIM. Visual inspection conducted for artifacts.
• Evaluated in clinical settings with cooperation partners.
• Seven peer-reviewed publications (427 patients on 1.5T and 3T systems, covering prostate, abdomen, liver, knee, hip, ankle, shoulder, hand and lumbar spine).
• Publications concluded beneficial use for clinical routine imaging.
• No reported cases of misinterpretation, altered, suppressed, or introduced anatomical information.
• Significant time savings reported in most cases by enabling faster image acquisition. |
  | For Deep Resolve Sharp:
• Successful passing of quality metrics tests (PSNR, SSIM, perceptual loss)
• In-house visual rating
• Evaluation of image sharpness by intensity profile comparisons of reconstruction with and without Deep Resolve Sharp | Deep Resolve Sharp:
• Impact characterized by PSNR, SSIM, and perceptual loss.
• Verified and validated by in-house tests, including visual rating and evaluation of image sharpness by intensity profile comparisons.
• Both tests showed increased edge sharpness. |

2. Sample sized used for the test set and the data provenance:

The document mixes "training" and "validation" datasets. It doesn't explicitly refer to a separate "test set" for regulatory evaluation with clear sample sizes for that purpose. The "Test statistics and test results" section refers to general evaluations and published studies.

• "Validation" Datasets (internal validation, not explicitly a regulatory test set):
  • Deep Resolve Boost: 1,874 2D slices
  • Deep Resolve Sharp: 2,057 2D slices
• Data Provenance (Training/Validation):
  • Source: For Deep Resolve Boost: "in-house measurements and collaboration partners." For Deep Resolve Sharp: "in-house measurements."
  • Origin: Not specified by country.
  • Retrospective/Prospective: "Input data was retrospectively created from the ground truth by data manipulation and augmentation" (for Boost) and "retrospectively created from the ground truth by data manipulation" (for Sharp). This implies the underlying acquired datasets were retrospective.
• "Clinical Settings" / Publications (Implied real-world evaluation, not a regulatory test set):
  • Deep Resolve Boost: "a total of seven peer-reviewed publications 427 patients"
  • Data Provenance: Not specified by origin or retrospective/prospective for these external evaluations.

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 in the document. It mentions "visual inspection" and "visual rating," but does not detail the number or qualifications of experts involved in these processes for the "validation" sets or any dedicated regulatory "test set." For the "seven peer-reviewed publications," the expertise of the authors is implied but not detailed as part of the regulatory submission.

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

This information is not provided in the document.

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:

A formal MRMC comparative effectiveness study demonstrating human reader improvement with AI assistance is not described in this document. The document focuses on the technical performance of the AI features themselves and their general clinical utility as reported in external publications (e.g., faster imaging, no misinterpretation), but not a comparative study of human performance with and without the AI.

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

Yes, the sections on "Test statistics and test results" for both Deep Resolve Boost and Deep Resolve Sharp describe evaluation of the algorithm's performance using quality metrics (PSNR, SSIM, perceptual loss) and visual/intensity profile comparisons. This implies standalone algorithm evaluation. No specific quantifiable results for these metrics are provided as acceptance criteria, only that tests were successfully passed and showed increased sharpness for Deep Resolve Sharp.

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

The ground truth for the AI training and validation datasets is described as:

• Deep Resolve Boost: "The acquired datasets represent the ground truth for the training and validation. Input data was retrospectively created from the ground truth by data manipulation and augmentation." This implies that the original, full-quality MR images serve as the ground truth.
• Deep Resolve Sharp: "The acquired datasets represent the ground truth for the training and validation. Input data was retrospectively created from the ground truth by data manipulation." Similarly, the original, high-resolution MR images are the ground truth.

This indicates the ground truth is derived directly from the originally acquired (presumably high-quality/standard) MRI data, rather than an independent clinical assessment like pathology or expert consensus. The AI's purpose is to reconstruct a high-quality image from manipulated or undersampled input, so the "truth" is the original high-quality image.

8. The sample size for the training set:

• Deep Resolve Boost: 24,599 2D slices
• Deep Resolve Sharp: 11,920 2D slices

Note that the document states: "due to reasons of data privacy, we did not record how many individuals the datasets belong to. Gender, age and ethnicity distribution was also not recorded during data collection."

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

As described in point 7:

• Deep Resolve Boost: The "acquired datasets" (original, full-quality MR images) served as the ground truth. Input data for the AI model was then "retrospectively created from the ground truth by data manipulation and augmentation," including undersampling, adding noise, and mirroring k-space data.
• Deep Resolve Sharp: The "acquired datasets" (original MR images) served as the ground truth. Input data was "retrospectively created from the ground truth by data manipulation," specifically by cropping k-space data so only the center part was used as low-resolution input, with the original full data as the high-resolution output/ground truth.

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Your MAGNETOM MR 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 extremittes. 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 vield information that may assist in diagnosis.

Your MAGNETOM MR 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.

Software syngo MR XA12M is the latest software version for MAGNETOM Amira and MAGNETOM Sempra. It supports the existing "A Tim+Dot system" configuration for MAGNETOM Amira and MAGNETOM Sempra, and the newly introducted "A BioMatrix system" configuration for MAGNETOM Amira. Software version syngo MR XA12M for MAGNETOM Amira and MAGNETOM Sempra includes software applications migrated from the secondary predicate device MAGNETOM Sola with syngo MR XA11A (K181322). Only minor adaptations were needed to support the system specific hardware and optimize the sequences/protocols. In addition, new software features, Segmented TOF, HASTE with variable flip angle, SMS in RESOLVE and QDWI, are also introduced in syngo XA12M. The device also includes hardware updates such as new/modified coils and other components.

This document describes the 510(k) premarket notification for the Siemens MAGNETOM Amira and MAGNETOM Sempra Magnetic Resonance Diagnostic Devices (MRDD) with software syngo MR XA12M. The submission aims to demonstrate substantial equivalence to previously cleared predicate devices.

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria for this device are not explicitly stated in terms of specific performance metrics (e.g., sensitivity, specificity, accuracy). Instead, substantial equivalence is claimed based on adherence to recognized standards, verification and validation testing, and image quality assessments. The reported device performance is broadly presented as performing "as intended" and exhibiting an "equivalent safety and performance profile" to the predicate devices.

The table below summarizes the technological changes and the general assessment of their performance as described in the submission:

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

The document does not specify a distinct "test set" with a quantifiable sample size (e.g., number of patients or images). The evaluation relies on "sample clinical images" for the new coils and software features. The provenance of this data (e.g., country of origin, retrospective or prospective collection) is also not detailed.

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

The document does not explicitly state the number of experts used or their specific qualifications for establishing ground truth for the "sample clinical images." The indication for use mentions that "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." This implies that the interpretation of images, including those used in performance testing, would be by a "trained physician," but no specific details are provided about the number or expertise of such individuals in the context of validating the device features.

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1, none) for the "sample clinical images" used in performance testing.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No MRMC comparative effectiveness study is mentioned. The submission focuses on demonstrating substantial equivalence through non-clinical performance testing and adherence to standards, rather than evaluating the improvement of human readers with or without AI assistance.

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

The primary purpose of the device (MAGNETOM MR system) is to produce images, and the software features enhance image acquisition and processing. The performance testing described (image quality assessments, software verification and validation) evaluates the algorithm's output (images) in a standalone manner prior to a physician's interpretation. However, the device's indications for use inherently involve human interpretation ("when interpreted by a trained physician"). The document does not describe a purely "algorithm-only" performance assessment in the context of clinical decision-making, as the device's function is to aid diagnosis by a human.

7. The Type of Ground Truth Used

The type of ground truth for the "sample clinical images" is not explicitly stated. Given that no clinical trials were conducted, it's highly probable that qualitative "image quality assessments" were made by internal experts or against known phantom/in-vivo characteristics, and potentially compared to images from the predicate device. There is no mention of pathology, expert consensus (beyond general physician interpretation), or outcomes data being used as ground truth for this submission.

8. The Sample Size for the Training Set

The document does not mention a "training set" in the context of machine learning or AI algorithms. The changes are largely software and hardware updates, along with the integration of existing features from a predicate device. If any of the new features (e.g., Segmented ToF, HASTE with variable flip angle) involve learned components, the training set size and characteristics are not disclosed.

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

Since no training set is discussed or implied for machine learning, the method for establishing ground truth for a training set is not described. The software and hardware updates appear to be based on engineering development and optimization rather than machine learning model training.

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