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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 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.

Compressed Sensing GRASP-VIBE is intended to be used in dynamic and/or non-contrast liver examinations to support patients who cannot reliably hold their breath for a conventional breath-hold measurement.

The subject device, MAGNETOM Vida, is an MR system. The software syngo MR XA10A is the latest software for the Siemens MAGNETOM Vida and includes software sequences, applications, coils and other hardware for the MAGNETOM scanner. The software sequences, applications, coils and other hardware were previously cleared with K170396.

MAGNETOM Vida will be offered ex-factory (new production) with software syngo MR XA10A and Compressed Sensing GRASP-VIBE. Installed MAGNETOM Vida systems can be updated by activating the blocked license.

This filing describes the new imaging feature intended to be used with the MAGNETOM Vida. Compressed Sensing GRASP-VIBE is intended to be used in dynamic and/or non-contrast liver examinations to support patients who cannot reliably hold their breath for a conventional breath-hold measurement.

The provided text describes a 510(k) premarket notification for the Siemens MAGNETOM Vida with Compressed Sensing GRASP-VIBE. The document asserts substantial equivalence to a predicate device and does not detail specific acceptance criteria or a comprehensive study proving the device meets them in the way clinical trials typically do. Instead, it relies on demonstrating that the new feature does not introduce new safety or effectiveness concerns.

Therefore, many of the requested items (e.g., sample size for test set, number of experts for ground truth, adjudication method, MRMC study, sample size for training set, ground truth for training set) are not explicitly addressed or applicable in the context of this 510(k) submission, which focuses on device modifications and substantial equivalence to an already cleared predicate.

However, based on the limited information available, here's what can be extracted and inferred:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not provide a table of quantitative acceptance criteria for image quality or clinical performance that would typically be found in a clinical study report. Instead, the "acceptance criteria" are implicitly met by demonstrating substantial equivalence to the predicate device through non-clinical testing and qualitative assessment of sample clinical images.

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

• Sample Size: Not explicitly stated for a dedicated clinical "test set." The document mentions "sample clinical images" were included in the determination of substantial equivalence, but not the number of images or patients.
• Data Provenance: Not specified. Given it's a Siemens product, it's likely international data, but no country of origin is mentioned. The type of data (retrospective or prospective) is also not specified for these "sample clinical images."

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. The document states that the images and physical parameters, "when interpreted by a trained physician yield information that may assist in diagnosis." For the "sample clinical images" used, it does not specify how many experts reviewed them nor their qualifications or the process of establishing ground truth.

4. Adjudication Method for the Test Set

• Not specified. This level of detail is typically not included in a 510(k) summary focused on substantial equivalence through non-clinical data and general claims.

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 is mentioned. This submission focuses on the performance of the device itself (specifically the new imaging feature) in relation to a predicate device, not on comparing human reader performance with or without the device's assistance.

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

• The document implies a standalone assessment of the new imaging feature's technical performance. It states: "A comparison of the functionality was performed between the new feature and the reference device feature by detailed simulations with a numerical phantom." This indicates an algorithm-only evaluation for technical characteristics, though not a clinical standalone performance study in the sense of diagnostic accuracy.

7. The Type of Ground Truth Used

• For the "detailed simulations with a numerical phantom," the ground truth would be the known properties of the numerical phantom.
• For the "sample clinical images," the type of ground truth is not explicitly stated. However, medical image interpretation typically relies on expert consensus, clinical follow-up, or pathology reports for ground truth in diagnostic accuracy studies. This document only mentions "interpretation by a trained physician" assisting in diagnosis, without specifying how ground truth was established for comparison.

8. The Sample Size for the Training Set

• Not applicable/mentioned. This is a 510(k) submission for an MRI system and its software feature, not a machine learning algorithm that underwent a separate training phase with a distinct training set. The "Compressed Sensing GRASP-VIBE" is a technical imaging feature, not an AI diagnostic algorithm in the typical sense that would require a ground-truthed training set for learning.

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

• Not applicable/mentioned, as there is no indication of a "training set" in the context of an AI algorithm learning from data.

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