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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.

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

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

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.

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

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.

Ask a specific question about this device

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.

The subject devices, MAGNETOM Sola and MAGNETOM Altea with software syngo MR XA61A, consist of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Sola with syngo MR XA51A (K221733).

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

Hardware
Modified Hardware:

• Host computers ((syngo MR Acquisition Workplace (MRAWP) and syngo MR Workplace (MRWP))
• MaRS (Measurement and Reconstruction System) computer – for MAGNETOM Sola only
• myExam 3D Camera

Software
New Features and Applications:

• GRE_PC
• Physiologging
• Deep Resolve Boost HASTE
• Deep Resolve Boost EPI Diffusion
• Complex Averaging
• myExam Implant Suite

Modified Features and Applications:

• OpenRecon Framework.
• BEAT_nav (re-naming only).
• Low SAR Protocols – SAR adoptive MR protocols 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 provided text describes the Siemens Medical Solutions USA, Inc. MAGNETOM Sola and MAGNETOM Altea with software syngo MR XA61A, which are Magnetic Resonance Diagnostic Devices (MRDD). The submission (K232535) claims substantial equivalence to a predicate device (MAGNETOM Sola with syngo MR XA51A, K221733).

Based on the provided information, the acceptance criteria and study details for the AI features (Deep Resolve Boost and Deep Resolve Sharp) are as follows:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Deep Resolve Boost:
  • TSE: more than 25,000 slices (implicitly for both training/validation and testing, as no separate test set is explicitly mentioned).
  • HASTE: more than 10,000 HASTE slices (refined from TSE dataset).
  • EPI Diffusion: more than 1,000,000 slices.
  • Data Provenance: Retrospective creation from acquired datasets. The data "covered a broad range of body parts, contrasts, fat suppression techniques, orientations, and field strength." Country of origin is not specified but given the manufacturer (Siemens Healthcare GmbH, Germany, and Siemens Shenzhen Magnetic Resonance LTD, China) and typical medical device development, it likely includes international data.
• Deep Resolve Sharp:
  • 2D images: more than 10,000 high resolution 2D images.
  • Data Provenance: Retrospective creation from acquired datasets. The data "covered a broad range of body parts, contrasts, fat suppression techniques, orientations, and field strength." Country of origin is not specified.

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

The document does not specify the number of experts or their qualifications for establishing ground truth for the test set specifically. It mentions that "visual comparisons" and "visual rating" were part of the evaluation for both Deep Resolve Boost and Deep Resolve Sharp, which implies human expert review. However, details about these experts are not provided.

4. Adjudication Method for the Test Set

The document does not explicitly state an adjudication method (e.g., 2+1, 3+1). It refers to "visual comparisons" and "visual rating" as part of the evaluation, which suggests expert review, but the process for resolving disagreements or reaching consensus is not detailed.

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 involving human readers with and without AI assistance is reported for the substantial equivalence submission. The non-clinical tests focus on performance metrics and visual comparisons of image quality produced by the AI feature versus predicate device features. The "Publications" section lists several clinical feasibility studies, but these are noted as "referenced to provide information" and are not direct evidence of human reader improvement with AI for this specific submission's evaluation. The submission states, "No 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

Yes, standalone performance was evaluated through quantitative image quality metrics (PSNR, SSIM, perceptual loss) and direct comparison of images produced by the AI-enhanced sequences against the predicate device's features. The "Test Statistics and Test Results Summary" for both Deep Resolve Boost and Deep Resolve Sharp detail these algorithm-only evaluations.

7. The Type of Ground Truth Used

The ground truth for both Deep Resolve Boost and Deep Resolve Sharp was established from acquired datasets (raw MRI data). This data was then retrospectively manipulated to create input data for the models:

• Deep Resolve Boost: Input data was "retrospectively created from the ground truth by data manipulation and augmentation," including undersampling k-space lines, lowering SNR, and mirroring k-space data. The acquired datasets themselves "represent the ground truth for the training and validation."
• Deep Resolve Sharp: Input data was "retrospectively created from the ground truth by data manipulation," specifically by cropping k-space data to use only the center part, which created corresponding low-resolution input data and high-resolution output/ground truth data. The acquired datasets "represent the ground truth for the training and validation."

Essentially, the "ground truth" refers to the high-quality, fully sampled/non-accelerated raw or reconstructed MRI data from which the training and validation inputs were derived.

8. The Sample Size for the Training Set

The sample sizes mentioned under "Training and Validation data" are implicitly for training, as they refer to the datasets from which both training and validation data were derived:

• Deep Resolve Boost:
  • TSE: more than 25,000 slices
  • HASTE: more than 10,000 HASTE slices (refined)
  • 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

The ground truth for the training set was established from acquired datasets (raw MRI data). As explained in point 7, this involved:

• Deep Resolve Boost: Using the acquired datasets as the "ground truth." Input data for training was then generated by manipulating this ground truth (e.g., undersampling, adding noise).
• Deep Resolve Sharp: Using the acquired datasets as the "ground truth." Input data was then generated by manipulating the k-space data of the ground truth to create corresponding low-resolution inputs and high-resolution ground truth outputs for the model.

Ask a specific question about this device

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.

The subject devices, MAGNETOM Sola, MAGNETOM Altea, MAGNETOM Sola Fit with software syngo MR XA51A, consist of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Vida with syngo MR XA50A (K213693).

This FDA 510(k) summary describes the Siemens Medical Solutions USA, Inc. MAGNETOM Sola, MAGNETOM Altea, MAGNETOM Sola Fit with syngo MR XA51A, a magnetic resonance diagnostic device (MRDD).

Here's an analysis of the provided text for acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not explicitly state a specific "test set" in the context of clinical images or patient data for evaluating the performance of the new/modified features. The performance testing conducted was primarily focused on non-clinical tests (software, electrical, mechanical, EMC).

Therefore:

• Sample size for the test set: Not applicable and not specified for clinical performance evaluation.
• Data Provenance: Not applicable and not specified. The document only mentions "sample clinical images were provided" but doesn't detail their use in performance testing or their origin.

3. Number of Experts Used to Establish Ground Truth and Qualifications

Not applicable. This document focuses on the technical safety and performance of the MR system itself (hardware and software functionalities) rather than the diagnostic accuracy of an AI algorithm on patient data. Therefore, there's no mention of experts establishing a "ground truth" for a test set of images.

4. Adjudication Method for the Test Set

Not applicable for the reasons mentioned above.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No. The document explicitly states: "No additional clinical tests were conducted to support substantial equivalence for the subject devices..." and "no additional clinical publications were needed referenced to provide information on the use of the following features and functions, since this was sufficiently done for the predicate device."

Therefore, an MRMC study was not conducted or referenced for these new/modified features.

6. Standalone Performance Study (Algorithm Only)

No. The document's performance testing section focuses on the integrated system's safety and functionality (software V&V, electrical, mechanical, EMC). There is no mention of a standalone algorithm performance study for a diagnostic task.

7. Type of Ground Truth Used

Not applicable. The "ground truth" concept (e.g., expert consensus, pathology, outcome data) typically applies to the evaluation of diagnostic algorithms against a gold standard. The reported performance tests are for the safety and functionality of the MR system and its software, where the "ground truth" is adherence to engineering specifications and regulatory standards.

8. Sample Size for the Training Set

Not applicable. The document describes the device as a magnetic resonance diagnostic device, not an AI-powered diagnostic algorithm that requires a "training set" of data in the machine learning sense. The "training" implied in the context of "myExam Autopilot" is about simplifying human user interaction, not about training a diagnostic AI model.

9. How Ground Truth for the Training Set Was Established

Not applicable for the reasons mentioned above.

Ask a specific question about this device

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.

Ask a specific question about this device

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

Your MAGNETOM system is indicated for use as a magnetic 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, vield 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 display and MR-Safe biopsy needles.

MAGNETOM Sola, MAGNETOM Altea and MAGNETOM Sola Fit with software syngo MR XA20A includes new and modified hardware and software compared to the predicate device, MAGNETOM Sola with software syngo MR XA11A. A high level summary of the hardware and software is provided below:

Hardware

• Computer
• Nose Marker for Inline Motion Correction
  Coils
• BM Body 18: The new BM Body 18 coil is a receive coil with 18 elements and is based on the Body 18 coil, (cleared with K101347). It is a general purpose coil.
  The BM Body 18 coil can be used with two different cables of different length; this capability was introduced with the BM Body 12 coil.

Software
Features and Applications

• SMS for TSE DIXON: Simultaneous excitation and acquisition of multiple slices with the Simultaneous multi-slice (SMS) technique for TSE Dixon imaging.
• GOLiver is a set of optimized pulse sequence for fast and efficient imaging of the abdomen / liver. It is designed to provide consistent exam slots and to reduce the workload for the user in abdominal / liver MRI.
• Angio TOF with Compressed Sensing (CS): The Compressed Sensing (CS) functionality is now available for TOF MRA within the BEAT pulse sequence type for the 1.5 T MR systems. Scan time can be reduced by an incoherent undersampling of k-space data. The usage of CS as well as the acceleration factor and further options can be freely selected by the user.
• RT Respiratory self-gating for FL3D VIBE: Non-contrast abdominal and thoracic examination in free breathing with reduced blur induced by respiratory motion.
• i SMS for RESOLVE and QDWI: Simultaneous excitation and acquisition of multiple slices with the Simultaneous multi-slice (SMS) technique for readout-segmented echo planar imaging (RESOLVE) and quiet diffusion weighted imaging (QDWI).
• SPACE with Compressed Sensing (CS): The Compressed Sensing (CS) functionality is now available for the SPACE pulse sequence type. Scan time can be reduced by the incoherent under-sampling of the k-space data. The usage of CS as well as the acceleration factor and other options can be freely selected by the user.
• SEMAC: SEMAC is a method for metal artifact correction in ortho imaging of patients with whole joint replacement. Using Compressed Sensing the acquisition can be accelerated.
• TSE MDME: A special variant of the TSE pulse sequence type which acquires several contrasts (with different TI and TE, i.e. Multi Delay Multi Echo) within a single sequence.
• TFL (3D MPRAGE), TSE and GRE with Inline Motion Correction: 3D -MPRAGE, TSE and GRE with Inline Motion Correction: Tracking of motion of the head during head scans with a nose marker and a camera system. The MR system uses the tracking information to compensate for the detected motion.
• EP SEG PHS: pulse sequence type EP SEG PHS, based on BEAT EPI and modified with a silent period that can be used by external devices/applications for synchronization with the MR imaging
• GRE PHS: pulse sequence type GRE PHS, is a GRE pulse sequence type, modified to provide a silent period that can be used by external devices/applications for synchronization with the MR imaging.
• GRE Proj: The GRE projection pulse sequence type "" allows the acquisition of 1-D projection data for different orientations.
• GOKnee2D: GOKnee2D is a set of multi-band pulse sequence types with Simultaneous Multislice TSE for fast and efficient imaging of the knee. It is designed to provide consistent exam slots and to reduce the workload for the user in Knee MRI.
• BEAT_interactive: The BEAT_Interactive pulse sequence type is a modification of the BEAT IRTTT pulse sequence type in order to interactively increase the slice thickness and switch on and off a magnetization pulse that the user can select prior to the measurement start.
• EP2D SE MRE: As an alternative of greMRE, EP2D SE MRE pulse sequence type is based on single-shot EP2D_SE_MRE sequence. It offers acquisition of multiple slices in a single, short breath-hold, and it is more robust against signal dephasing effects while providing comparable relative stiffness values.
• ZOOMit DWI: syngo ZOOMit based on EPI diffusion allows diffusion weighted imaging (DWI) while avoiding signal and artifacts from surrounding tissue. The feature is now available for 1-ch-systems and enables improved robustness to infolding artifacts from tissue from outside the excited reqion.
• SPACE Flair Improvements: SPACE pulse sequence type offers a magnetization preparation mode for brain imaging with FLAIR contrast (FLuid Attenuated Inversion Recovery); improving the image quality of FLAIR images.
• External Phase Correction Scan for EPI Diffusion: Separate N/2 Nyquist ghost correction acquisition method for diffusion imaging in the presence of fat.
• MR Breast Biopsy Workflow improvements: The changes made to MR Breast Biopsy application target two areas: the improved readability of planning results and the ability to handle the planning of multiple biopsy targets.
• GOBrain / GOBrain+: GOBrain (brain examination in short acquisition time) GOBrain+ (adaptation of GOBrain pulse sequences)

Software / Platform

• Dot Cockpit: MR Protocol Manager as part of a scanner fleet with connection via a share.
• Access-i: The interface Access-i allows 3rd party devices to establish a bidirectional communication with the MR scanner via a secure local network connection, supporting data transfer to and triggering of data acquisition from the 3rd party device. It enables the 3rd party client to control and edit a measurement program on the MR.
• Table positioning mode: A new table positioning mode "FIX" is introduced which complements the existing table positioning modes ISO and LOC to support workflows in which the user needs to be in control of a defined Zposition at which measurements get executed.

Other Modifications and / or Minor Changes

• MAGNETOM Sola Fit: The MAGNETOM Sola Fit is a new MRI System which is the result of an upgrade from a MAGNETOM Aera.
• BM Body 12: For MR examinations of head and neck in situations where a rigid rf head coil cannot be used, e.g. with patients positioned in thermoplastic masks used for radiotherapy planning, aiming at higher signal-to-noise and spatial resolution as what can be achieved with 4-channel Flex rf coils
• Body 18: For MR examinations of head and neck in situations, where a rigid rf head coil cannot be used, e.g. with patients positioned in thermoplastic masks used for radiotherapy planning, aiming at higher signal-to-noise and spatial resolution than what can be achieved with 4-channel Flex rf coils
• UltraFlex Large 18, UltraFlex Small 18: For MR examinations of head and neck in situations, where a rigid rf head coil cannot be used, e.g. with patients positioned in thermoplastic masks used for radiotherapy planning, aiming at higher signal-to-noise and spatial resolution than what can be achieved with 4channel Flex rf coils
• Broad band / narrow band online supervision: The broadband/narrowband supervision checks the correctness of the measurement values used for the SAR calculation. With syngo MR XA20A, the supervision cycle is reduced significantly.
• LiverLab Dot Engine debundling: LiverLab is now offered separately as standalone workflow and is also still available as part of the Abdomen Dot Engine.
• The 1.5T system MAGNETOM Altea is made available to the marked with software syngo MR XA20A.

This document is a 510(k) summary for the Siemens MAGNETOM Sola, Altea, and Sola Fit MRI systems with software syngo MR XA20A. It outlines their substantial equivalence to a predicate device.

Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided text:

Important Note: This document primarily focuses on demonstrating substantial equivalence to a previously cleared predicate device for a Magnetic Resonance Diagnostic Device (MRDD). The testing described is largely for demonstrating the safety and performance of new and modified hardware and software features in comparison to the predicate. It is not a clinical study proving diagnostic accuracy of an AI algorithm, a typical scenario for the detailed acceptance criteria you requested. Therefore, many of your specific questions regarding AI algorithm performance (e.g., MRMC studies, ground truth for training data, effect size of human improvement with AI) are not applicable or not explicitly detailed in this type of 510(k) submission for an MRI system.

The "acceptance criteria" here relate more to the performance and safety of the MRI system itself, rather than diagnostic accuracy of an AI algorithm based on a specific clinical endpoint.

Acceptance Criteria and Reported Device Performance

The document presents the testing conducted to support the substantial equivalence of the new and modified hardware and software components of the MAGNETOM systems. The "acceptance criteria" are implied by the successful completion of these nonclinical tests and their demonstration that the device performs as intended and is equivalent to the predicate.

Table of Acceptance Criteria and Reported Device Performance (Implied from Nonclinical Tests):

Study Details (Based on Provided Text)

Given that this is a 510(k) for an MRI system with new/modified features, and not an AI diagnostic algorithm, the "study" is a collection of nonclinical tests.

1. Sample size used for the test set and the data provenance:

   • The document states "Sample clinical images were provided" for image quality assessment. It does not specify the number of images or patients (sample size) used for these assessments.
   • Data provenance (country of origin, retrospective/prospective) is not specified.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • For the "Image quality assessments by sample clinical images," it's stated, "when interpreted by a trained physician, yield information that may assist in diagnosis."
   • However, the number and qualifications of experts involved in the assessment of these sample clinical images for the purpose of the 510(k) submission are not specified. This is likely an internal verification step, not a formal clinical trial with external readers.

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

   • Not specified. Given the nature of the nonclinical testing for device features, a formal adjudication process for "ground truth" (as expected for diagnostic performance studies) is not described. The assessments were likely internal comparisons to predicate performance.

4. 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 study described. This 510(k) is for an MRI system, not an AI diagnostic algorithm. The improvements mentioned ("fast and efficient imaging," "reduce the workload") are theoretical benefits of the features themselves, not a quantified improvement in human reader performance with AI assistance. The document explicitly states "No additional clinical tests were conducted to support substantial equivalence for the subject devices."

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

   • Not applicable. This document describes an MRI system, not a standalone AI algorithm. The software features are integrated into the system for image acquisition and processing.

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

   • The term "ground truth" as it pertains to clinical diagnostic accuracy is not explicitly used or established in this context. The "truth" for these nonclinical tests is based on the device meeting its engineering specifications, performing equivalently to the predicate, and producing images of acceptable quality when interpreted by a trained physician. The images themselves serve as the output, assessed against expected image quality parameters.

7. The sample size for the training set:

   • Not applicable / Not specified. This document describes a medical device (MRI system) with software and hardware features, not a machine learning model that requires a "training set" in the common sense. Any internal development data used to refine pulse sequences or image reconstruction is not considered a "training set" in the context of AI regulatory submissions.

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

   • Not applicable / Not specified. See point 7.

Ask a specific question about this device

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 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 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 Sola with XJ gradient system is similar to the predicate device MAGNETOM Aera with syngo MR E11C (K153343) except for some new and modified software and hardware.

The provided text describes the Siemens MAGNETOM Sola MRI system and its substantial equivalence to a predicate device, but it does not contain specific acceptance criteria for a device's performance (e.g., accuracy, sensitivity, specificity) or a detailed study proving such criteria are met in the context of, for example, an AI/algorithm-based diagnostic aid.

The document mainly focuses on the regulatory submission for premarket notification (510(k)) of a new MRI system, detailing its hardware and software components, and asserting its safety and effectiveness based on equivalence to existing devices.

Therefore, most of the requested information regarding acceptance criteria, device performance, sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC studies, or standalone algorithm performance, cannot be extracted from this document.

However, I can provide the following based on the available text:

1. Table of Acceptance Criteria and Reported Device Performance: Not available in the provided document in the context of a diagnostic performance study. The document primarily discusses performance in terms of achieving substantial equivalence for the overall MRI system, not specific diagnostic outcomes.

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

   • Sample Size: A clinical study of 40 individuals was conducted.
   • Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). This study was specifically to determine nerve stimulation thresholds for the gradient system output, not for diagnostic image interpretation performance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. The clinical study mentioned was for nerve stimulation thresholds, not for establishing ground truth for diagnostic image interpretation.

4. Adjudication method for the test set: Not applicable based on the type of study mentioned (nerve stimulation thresholds).

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, an MRMC comparative effectiveness study is not mentioned. The device described is an MRI scanner, and the focus is on its hardware and software advancements for image acquisition and processing, not an AI-powered diagnostic interpretation tool for which such a study would typically be conducted.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: No, standalone algorithm performance is not discussed. The device is a full MRI system, not a standalone algorithm.

7. The type of ground truth used: For the 40-individual study, the ground truth was the observed parameters related to nerve stimulation. It was used to set the Peripheral Nerve Stimulation (PNS) threshold level.

8. The sample size for the training set: Not applicable. This document does not describe a machine learning algorithm's training set for diagnostic purposes. The software features described are part of the MRI system's operational software.

9. How the ground truth for the training set was established: Not applicable.

Summary of what is available from the document:

• The document is a 510(k) premarket notification for the Siemens MAGNETOM Sola MRI system.
• It describes new and modified hardware and software features compared to a predicate device.
• Nonclinical tests included:
  • Sample clinical images for coils.
  • Software verification and validation per FDA guidance.
  • Performance tests per FDA guidance for MRDDs.
  • Hardware modification verification & validation.
• Clinical tests involved a study of 40 individuals to determine nerve stimulation thresholds to limit gradient system output, which informed the PNS threshold level required by IEC 60601-2-33. No other clinical tests were conducted to support substantial equivalence for diagnostic performance, though sample clinical images were provided for new coils.
• The device is claimed to be substantially equivalent to the predicate device (MAGNETOM Aera with syngo MR E11C) based on having the same intended use and different technological characteristics that bear an equivalent safety and performance profile.
• The document lists various standards (IEC, ISO, NEMA) to which the device conforms for safety and performance, including software life cycle processes (IEC 62304:2006).

Ask a specific question about this device

Your MAGNETOM system is indicated for use as a magnetic device (MRDD) that produces transverse, sagittal, coronal and oblique cross sectional 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 inages 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 Sola with software syngo MR XA11A is similar to the previous cleared predicate device MAGNETOM Aera with syngo MR E11C (K153343) but includes new and modified hardware and software compared to MAGNETOM Aera. A high level summary of the hardware and software changes is included below.

The provided text describes the Siemens MAGNETOM Sola, a Magnetic Resonance Diagnostic Device (MRDD), and its journey through FDA clearance via a 510(k) premarket notification (K181322). The submission argues for substantial equivalence to a predicate device, MAGNETOM Aera (K153343). However, the document does not include a table of acceptance criteria or report device performance against specific metrics as requested. It outlines the scope of changes, safety testing, and refers to clinical images and a specific clinical study for nerve stimulation thresholds, but it doesn't detail performance-based acceptance criteria for image quality or diagnostic accuracy in the way typically seen for AI/ML devices.

Here's an attempt to answer the questions based only on the provided text, highlighting where information is absent:

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

The document does not provide a table of acceptance criteria or specific reported device performance metrics against such criteria in the context of diagnostic accuracy or image quality improvements. The submission focuses on demonstrating substantial equivalence through:

• Similar intended use to the predicate device.
• Conformity to relevant standards (IEC, ISO, NEMA).
• Software verification and validation.
• Sample clinical images to support new/modified features.
• A clinical study to determine nerve stimulation thresholds for gradient system output.

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:
  • For the nerve stimulation thresholds study: 36 individuals.
  • For testing new/modified pulse sequences and algorithms, and supporting new coils/features: "Sample clinical images" were taken, but the exact number of cases or individuals is not specified.
• Data provenance: Not specified (e.g., country of origin, retrospective or prospective). The text only mentions "Sample clinical images were taken" and "A clinical study... was conducted."

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)

The document does not specify the number or qualifications of experts used to establish ground truth for image quality assessments or the clinical images provided. The nerve stimulation study likely involved medical professionals, but their role in "ground truth" establishment for diagnostic purposes is not detailed.

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

The document does not describe any adjudication method for the 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

A multi-reader multi-case (MRMC) comparative effectiveness study is not mentioned in the document. The device is a Magnetic Resonance Diagnostic Device, not explicitly an AI/ML-driven diagnostic aid that would directly assist human readers in interpretation or diagnosis in the context typically seen in MRMC studies for AI.

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

The document describes the MAGNETOM Sola as a "magnetic resonance diagnostic device" which produces images and/or spectra that, "when interpreted by a trained physician, yield information that may assist in diagnosis." This indicates a human-in-the-loop system, implying that a standalone "algorithm only" performance study for direct diagnostic output was not the primary focus or perhaps applicable in the traditional sense for this device submission which is for the MR system itself rather than an AI-driven interpretation tool. However, the software verification and validation are for the algorithm within the system.

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

The type of ground truth used for image quality assessments or for the "sample clinical images" is not explicitly stated. For the nerve stimulation study, the "observed parameters were used to set the PNS (Peripheral Nerve Stimulation) threshold level," which seems to be a physiological measurement rather than a diagnostic ground truth.

8. The sample size for the training set

The document does not mention a training set sample size. This type of information is typically provided for AI/ML models that undergo specific training, which isn't the primary focus of this MRDD system clearance description.

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

Since a training set is not mentioned, the method for establishing its ground truth is also not provided.

Ask a specific question about this device