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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 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 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.

The subject device, MAGNETOM Avanto and MAGNETOM Verio D with software syngo MR E11D, is a modification of the previously cleared predicate device, MAGNETOM Avanto and MAGNETOM Verio with software syngo MR D13A (K121434). Software version syngo E11D for MAGNETOM Avanto and MAGNETOM Verio includes software applications migrated from the previously cleared MAGNETOM Aera and Skyra systems with syngo MR E11C (K153343). Only minor adaptations were needed to support the system specific hardware and optimize the sequence/protocols. The description lists new and modified sequences/algorithms, new and modified applications, and new hardware components.

Here's the breakdown of the acceptance criteria and study information based on the provided text, specifically for the K181613 submission for MAGNETOM Avanto and MAGNETOM Verio with software syngo MR E11D:

1. Table of Acceptance Criteria and Reported Device Performance

It's important to note that the provided document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting detailed acceptance criteria and quantitative performance results for a new, novel AI algorithm. The performance is assessed primarily through verification and validation testing against established standards and equivalency to previously cleared predicate devices.

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

• Test Set Sample Size: The document does not explicitly state a specific sample size for a "test set" in the context of a prospective, human-read study with a defined dataset for performance evaluation. Instead, it mentions "non-clinical data" and "sample clinical images."
• Data Provenance: The document generally refers to "non-clinical data" and "sample clinical images." There is no information regarding the country of origin or whether the data was retrospective or prospective. The "sample clinical images" were provided "in accordance with the FDA guidance document, 'Submission of Premarket Notifications for Magnetic Resonance Diagnostic Devices,'" but the details of these images (e.g., number, origin) are not elaborated.

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

There is no information provided in the document regarding the number of experts or their qualifications for establishing ground truth for any specific test set. The submission focuses on the performance of the MR system itself, not necessarily an AI-driven image analysis tool requiring expert-adjudicated ground truth.

4. Adjudication Method for the Test Set

There is no information provided regarding any adjudication method, as the study described is not an AI performance study that typically involves expert adjudication.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs without AI Assistance

No, an MRMC comparative effectiveness study involving AI assistance for human readers was not done or reported in this 510(k) summary. The device in question is a magnetic resonance diagnostic device (MR system) and its updated software, not an AI-driven diagnostic algorithm used to assist human readers.

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

The document describes performance testing of the entire MR system and its software, not a standalone algorithm. The "new sequences or algorithms" and "applications" mentioned are integral parts of the MR system's functionality, producing images and spectra for interpretation by a trained physician. Therefore, a standalone algorithm-only performance study (as would be typical for an AI diagnostic device) was not performed or reported in this context.

7. The Type of Ground Truth Used

The concept of "ground truth" as typically defined for AI performance studies (e.g., pathology, outcomes data, expert consensus) is not explicitly addressed nor applied in this submission. The device's performance is evaluated against its intended function (producing images, spectra, and physical parameters) and its equivalence to a predicate device, which implies that the output (the images themselves) is the primary "truth" and is interpreted by a trained physician.

8. The Sample Size for the Training Set

Since this submission is for an updated MR system software and not a de novo AI algorithm, there is no mention or requirement for a "training set" in the context of machine learning. The software updates described include new and modified sequences or algorithms and applications, which are developed and validated through traditional software engineering and control methods, not machine learning model training.

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

As there is no training set discussed in this document for an AI algorithm, the question of how ground truth was established for it is not applicable.

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The MAGNETOM Verio 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. Depending on the region of interest, contrast agents may be used. These images and / or spectra when interpreted by a trained physician yield information that may assist in diagnosis.

The MAGNETOM Verio 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.

The MAGNETOM Verio comes with I-class and T-class releases based on syngo MR B15B. It is a 3 Tesla MR scanner based on a superconducting magnet. It consists of the same types of hardware (with a modified magnet, gradient coil and RF body coil) that are currently available with the MAGNETOM Trio a Tim system including Matrix Coils and Total Imaging Matrix (Tim) technology.

The provided text describes the 510(k) summary for the Siemens MAGNETOM Verio, a magnetic resonance diagnostic device. The submission focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study with specific acceptance criteria for performance against a clinical ground truth.

Therefore, many of the requested sections below cannot be fully populated as the information is not present in the provided document. The study described focuses on technical performance metrics (e.g., Signal-to-Noise Ratio, Geometric Distortion) to show equivalence to a predicate device, not on diagnostic accuracy based on a clinical ground truth.

Acceptance Criteria and Device Performance

The provided 510(k) summary does not present specific acceptance criteria in terms of clinical performance (e.g., sensitivity, specificity, accuracy) for a diagnostic task, nor does it report the device's performance against such criteria. Instead, it demonstrates substantial equivalence by comparing technical performance parameters to a predicate device.

The "acceptance criteria" can be inferred as showing comparable or better technical performance compared to the predicate device for various physical parameters.

Study Details

The provided document describes a technical study demonstrating substantial equivalence rather than a clinical study evaluating diagnostic performance.

1. Sample size used for the test set and the data provenance: Not applicable in the context of diagnostic accuracy. The performance measurements were done on the MAGNETOM Verio and the predicate MAGNETOM Trio a Tim System through technical evaluations. The specific sample sizes (e.g., number of scans, phantoms) used for each technical measurement are not detailed, nor is the "provenance" of such technical data mentioned beyond it being generated by the manufacturer.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. The "ground truth" here refers to established physical standards (IEC, ISO, NEMA) for safety and predefined measurements for performance. There were no human experts establishing a clinical ground truth for diagnostic purposes in this submission.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable. No clinical adjudication process was described or required for these technical performance metrics.

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: Not applicable. This submission is for an MRI scanner, not an AI-powered diagnostic algorithm. No MRMC study was mentioned.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an algorithm, but a medical device (MRI scanner). The "performance" assessment was for the device's physical and imaging capabilities.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): The "ground truth" for this submission was based on established engineering standards (IEC, ISO, NEMA) for safety parameters and direct physical measurements for performance parameters (e.g., SNR, geometric distortion) on the device and its predicate.

7. The sample size for the training set: Not applicable. This is not an AI/ML device requiring a training set. The device's design and manufacturing rely on established engineering principles and iterative development, not data-driven training in the AI sense.

8. How the ground truth for the training set was established: Not applicable. There was no "training set" or corresponding ground truth in the context of an AI/ML device.

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