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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.

Device Description

The subject device, MAGNETOM Avanto Fit with software syngo MR XA70A, consists of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Avanto Fit with syngo MR XA50A (K220151).

A high-level summary of the new and modified hardware and software is provided below:

For MAGNETOM Avanto Fit with syngo MR XA70:

Hardware

New Hardware:
myExam 3D Camera
BM Head/Neck 20

Modified Hardware:
Sanaflex (cushions for patient positioning)

Software

New Features and Applications:
myExam Autopilot Brain
myExam Autopilot Knee
3D Whole Heart
HASTE_interactive
GRE_PC
Open Recon
Deep Resolve Gain
Fleet Reference Scan
Physio logging
complex averaging
AutoMate Cardiac
Ghost Reduction
BLADE diffusion
Beat Sensor
Deep Resolve Sharp
Deep Resolve Boost and Deep Resolve Boost (TSE)
Deep Resolve Boost HASTE
Deep Resolve Boost EPI Diffusion

Modified Features and Applications:
SPACE improvement (high band)
SPACE improvement (incr grad)
Brain Assist
Eco power mode
myExam Angio Advanced Assist (Test Bolus)

The subject device, MAGNETOM Skyra Fit with software syngo MR XA70A, consists of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Skyra Fit with syngo MR XA50A (K220589).

A high-level summary of the new and modified hardware and software is provided below:

For MAGNETOM Skyra Fit with syngo MR XA70:

Hardware

New Hardware:
myExam 3D Camera

Modified Hardware:
Sanaflex (cushions for patient positioning)

Software

New Features and Applications:
Beat Sensor
HASTE_interactive
GRE_PC
3D Whole Heart
Deep Resolve Gain
Open Recon
Ghost Reduction
Fleet Reference Scan
BLADE diffusion
HASTE diffusion
Physio logging
complex averaging
Deep Resolve Swift Brain
Deep Resolve Sharp
Deep Resolve Boost and Deep Resolve Boost (TSE)
Deep Resolve Boost HASTE
Deep Resolve Boost EPI Diffusion
AutoMate Cardiac
SVS_EDIT

Modified Features and Applications:
SPACE improvement (high band)
SPACE improvement (incr grad)
Brain Assist
Eco power mode
myExam Angio Advanced Assist (Test Bolus)

The subject device, MAGNETOM Sola Fit with software syngo MR XA70A, consists of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Sola Fit with syngo MR XA51A (K221733).

A high-level summary of the new and modified hardware and software is provided below:

For MAGNETOM Sola Fit with syngo MR XA70:

Hardware

New Hardware:
myExam 3D Camera

Modified Hardware:
Sanaflex (cushions for patient positioning)

Software

New Features and Applications:
GRE_PC
3D Whole Heart
Ghost Reduction
Fleet Reference Scan
BLADE diffusion
Physio logging
Open Recon
Complex averaging
Deep Resolve Sharp
Deep Resolve Boost and Deep Resolve Boost (TSE)
Deep Resolve Boost HASTE
Deep Resolve Boost EPI Diffusion
AutoMate Cardiac
Implant suite

Modified Features and Applications:
SPACE improvement (high band)
SPACE improvement (incr grad)
Brain Assist
Eco power mode

The subject device, MAGNETOM Viato.Mobile with software syngo MR XA70A, consists of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Viato.Mobile with syngo MR XA51A (K240608).

A high-level summary of the new and modified hardware and software is provided below:

For MAGNETOM Viato.Mobile with syngo MR XA70:

Hardware

New Hardware:
n.a.

Modified Hardware:
Sanaflex (cushions for patient positioning)

Software

Modified Features and Applications:
SPACE improvement (high band)
SPACE improvement (incr grad)
Brain Assist
Eco power mode

Furthermore, the following minor updates and changes were conducted for the subject devices:

Low SAR Protocol minor update (for all subject devices but MAGNETOM Skyra Fit): the goal of the SAR adaptive protocols was to be able to perform knee, spine, heart and brain examinations with 50% of the max allowed SAR values in normal mode for head and whole-body SAR. The SAR reduction was achieved by parameter adaptations like Flip angle, TR, RF Pulse Type, Turbo Factor, concatenations. For cardiac clinically accepted alternative imaging contrasts are used (submitted with K232494).

Implementation of image sorting prepare for PACS (submitted with K231560).

Implementation of improved DICOM color support (submitted with K232494).

Needle intervention AddIn was added all subject device (submitted with K232494).

Inline Image Filter switchable for users: in the subject device, users have the ability to switch the "Inline image filter" (implicite Filter) on or off. This filter is an image-based filter that can be applied to specific pulse sequence types. The function of the filter remains unchanged from the previous device MAGNETOM Sola with syngo MR XA61A (K232535).

SVS_EDIT is newly added for MAGNETOM Skyra Fit, but without any changes (submitted with K203443)

Brain Assist received an improvement and is identical to that of snygo MR XA61A (K232535)

Open Recon is introduced for all systems. The function of Open Recon remains unchanged from the previous submissions (submitted with K221733).

Lock TR and FA in Bold received a minor UI update

Implant Suite is newly introduced for MAGNETOM Sola Fit and MAGNETOM Viato.Mobile, but without any changes (submitted with K232535)

myExam Autopilot Brain and myExam Autopilot Knee are newly introduced for the subject device MAGNETOM AVANTO Fit and are unchanged from previous submissions (submitted with K221733).

myExam Angio Advanced Assist (Test Bolus) received a bug fixing and minimal UI improvements.

AI/ML Overview

The provided text is an FDA 510(k) clearance letter for various MAGNETOM MRI Systems. While it details new and modified software and hardware features, it does not include specific acceptance criteria or a study that "proves the device meets the acceptance criteria" in terms of performance metrics like sensitivity, specificity, or accuracy for a diagnostic task.

Instead, the document focuses on demonstrating substantial equivalence to predicate devices. This is achieved by:

Stating that the indications for use are the same.
Listing numerous predicate and reference devices.
Detailing hardware and software changes.
Mentioning non-clinical tests like software verification and validation, sample clinical images, and image quality assessment to show that the new features maintain an "equivalent safety and performance profile" to the predicate devices.
Referencing scientific publications for certain features to support their underlying principles and utility.
Briefly describing the training and validation data for two AI features: Deep Resolve Boost and Deep Resolve Sharp, but without performance acceptance criteria or detailed results.

Therefore, much of the requested information cannot be extracted from this document because it is not a study report detailing clinical performance against predefined acceptance criteria for a specific diagnostic outcome.

However, I can extract the information related to the AI features as best as possible from the "AI Features/Applications training and validation" section (Page 16).

Acceptance Criteria and Study Details (Limited to AI Features)

1. Table of Acceptance Criteria and Reported Device Performance

Feature	Acceptance Criteria	Reported Device Performance
Deep Resolve Boost	(Not explicitly stated in the provided document as specific numerical thresholds, but implied through evaluation metrics.)	"The impact of the network has been characterized by several quality metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Most importantly, the performance was evaluated by visual comparisons to evaluate e.g., aliasing artifacts, image sharpness and denoising levels." (Exact numerical results not provided).
Deep Resolve Sharp	(Not explicitly stated in the provided document as specific numerical thresholds, but implied through evaluation metrics and verification activities.)	"The impact of the network has been characterized by several quality metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and perceptual loss. In addition, the feature has been verified and validated by inhouse tests. These tests include visual rating and an evaluation of image sharpness by intensity profile comparisons of reconstructions with and without Deep Resolve Sharp." (Exact numerical results not provided).

2. Sample size used for the test set and the data provenance

Deep Resolve Boost:
- Test Set Sample Size: Not explicitly stated as a separate "test set" size. The document mentions "training and validation data" for over 25,000 TSE slices, over 10,000 HASTE slices (for refinement), and over 1,000,000 EPI Diffusion slices. It's unclear what proportion of this was used specifically for final testing, or if the "validation" mentioned includes the final performance evaluation.
- Data Provenance: Retrospective, described as "Input data was retrospectively created from the ground truth by data manipulation and augmentation." Country of origin is not specified.
Deep Resolve Sharp:
- Test Set Sample Size: Not explicitly stated as a separate "test set" size. The document mentions "training and validation" on more than 10,000 high resolution 2D images. Similar to Deep Resolve Boost, it's unclear what proportion was specifically for final testing.
- Data Provenance: Retrospective, described as "Input data was retrospectively created from the ground truth by data manipulation." Country of origin is not specified.

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. The definition of "ground truth" for the AI features refers to the acquired datasets themselves rather than expert-labeled annotations. Visual comparisons are mentioned as part of the evaluation, but without details on expert involvement or qualifications.

4. Adjudication method for the test set

This information is not provided in the document. While "visual comparisons" and "visual rating" are mentioned, no specific adjudication method (e.g., 2+1, 3+1) is described.

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, a MRMC comparative effectiveness study demonstrating human reader improvement with AI assistance is not described in this document. The focus of the AI features (Deep Resolve Boost and Deep Resolve Sharp) is on image quality enhancement (denoising, sharpness) and reconstruction rather than assisting human readers in a diagnostic task that can be quantified by an effect size.

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

Yes, the evaluation of Deep Resolve Boost and Deep Resolve Sharp, based on metrics like PSNR, SSIM, and perceptual loss, and "visual comparisons" or "visual rating" appears to be an assessment of the algorithm's performance in enhancing image quality in a standalone capacity, without direct human-in-the-loop interaction for diagnosis.

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

Deep Resolve Boost: "The acquired datasets (as described above) represent the ground truth for the training and validation." This implies the original, full-quality, unaltered MRI scan data. Further, "Input data was retrospectively created from the ground truth by data manipulation and augmentation. This process includes further under-sampling of the data by discarding k-space lines, lowering of the SNR level by addition Restricted of noise and mirroring of k-space data."
Deep Resolve Sharp: "The acquired datasets represent the ground truth for the training and validation." Similar to Boost, this refers to original, high-resolution MRI scan data. For training, "k-space data has been cropped such that only the center part of the data was used as input. With this method corresponding low-resolution data as input and high-resolution data as output / ground truth were created for training and validation."

8. The sample size for the training set

Deep Resolve Boost:
- TSE: more than 25,000 slices
- HASTE (for refinement): more than 10,000 HASTE slices
- EPI Diffusion: more than 1,000,000 slices
Deep Resolve Sharp: more than 10,000 high resolution 2D images.

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

Deep Resolve Boost: The ground truth was established by the "acquired datasets" themselves (full-quality MRI scans). The training input data was then derived from this ground truth by simulating degraded images (e.g., under-sampling, adding noise).
Deep Resolve Sharp: Similarly, the ground truth was the "acquired datasets" (high-resolution MRI scans). The training input data was derived by cropping k-space data to create corresponding low-resolution inputs.

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