(64 days)
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 Lumina with software syngo MR XA11B includes modified hardware and software compared to the predicate device, MAGNETOM Vida with syngo MR XA11A. A high level summary of the modified features is provided below:
Hardware
Modified Hardware
- -Gradient system with XK gradient engine (36/200): Reduction in GPA performance with unchanged hardware components
- -Cover: Adapted system design
- Tim [180x32] configuration: patient table with 180 simultaneous connectable coil elements
Software
New Features and Applications
- GOLiver: Set of optimized pulse sequences 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.
Other Modifications and / or Minor Changes - Turbo Suite marketing bundle: Turbo Suite is a marketing bundle of components for accelerated MR imaging offered for the MAGNETOM Lumina MR system.
Here's a breakdown of the acceptance criteria and study information for the MAGNETOM Lumina device, based on the provided document:
This document does not describe the specific acceptance criteria or a detailed clinical study demonstrating the device's performance in a way that typically includes metrics like sensitivity, specificity, or AUC, as would be expected for an AI/algorithm-based diagnostic tool. Instead, this 510(k) summary focuses on demonstrating substantial equivalence to a predicate device (MAGNETOM Vida) through non-clinical testing and adherence to recognized standards.
The "device" in question (MAGNETOM Lumina) is a Magnetic Resonance Diagnostic Device (MRDD), an MRI scanner, not an AI-powered diagnostic algorithm in the sense of providing specific disease detection or quantification with performance metrics. The new software feature "GOLiver" within the MAGNETOM Lumina is described as a set of optimized pulse sequences for imaging, designed to improve workflow, not an AI for diagnosis.
Therefore, many of the requested elements (like effect size of AI assistance, sample size for test set with ground truth, expert qualifications for ground truth, adjudication methods) are not applicable or not provided in the context of this 510(k) submission, which is for an MRI scanner itself.
However, I can extract information related to the closest aspects of acceptance criteria and testing that are present:
Acceptance Criteria and Device Performance for MAGNETOM Lumina
Given that the device is an MRI system (not an AI diagnostic algorithm), the acceptance criteria and performance evaluation are centered on safety, functionality, and image quality compared to a predicate device, rather than diagnostic accuracy metrics of an AI.
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria Category | Specific Criteria (Implied/Stated) | Reported Device Performance (Summary from Document) |
|---|---|---|
| Safety & Essential Performance | Compliance with IEC 60601-1 series (basic safety & essential performance) | Conforms to ES60601-1:2005/(R) 2012 and A1:2012, and 60601-2-33 Ed. 3.2:2015. |
| Electromagnetic Compatibility (EMC) | Compliance with IEC 60601-1-2 (EMC requirements) | Conforms to 60601-1-2 Edition 4.0:2014-02. |
| Risk Management | Implementation of risk management process as per ISO 14971 | Compliance with ISO 14971 Second edition 2007-10 for identification and mitigation of potential hazards. |
| Usability Engineering | Application of usability engineering principles for medical devices | Conforms to 62366 Edition 1.0 2015. |
| Software Life Cycle Processes | Compliance with IEC 62304 (software life cycle processes) | Conforms to 62304:2006. Software verification and validation testing completed as per FDA guidance. |
| Image Quality (New Pulse Sequences - GOLiver) | Equivalent image quality between new pulse sequences and predicate device's pulse sequences. | Image quality assessment completed by comparing image quality, results demonstrate device performs as intended. |
| MRI Performance (General) | Compliance with FDA guidance "Submission of Premarket Notifications for Magnetic Resonance Diagnostic Devices." | Performance tests completed as per the specified FDA guidance. Results demonstrate device performs as intended. |
| Acoustic Noise Measurement | Compliance with NEMA MS 4-2010 | Conforms to MS 4-2010. |
| Characterization of Phased Array Coils | Compliance with NEMA MS 9-2008 | Conforms to MS 9-2008. |
| Digital Imaging and Communications in Medicine (DICOM) | Compliance with DICOM standards | Conforms to PS 3.1 - 3.20 (2016). |
| Biocompatibility | Compliance with ISO 10993-1 (biological evaluation of medical devices) | Conforms to 10993-1:2009/(R) 2013. |
| Intended Use | Device performs as intended for diagnosis of internal structure and/or function during various procedures. | Stated to have the same intended use as the predicate device. Non-clinical data suggests equivalent safety and performance profile. |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: Not explicitly stated as a number of patients or cases in the typical sense for an AI diagnostic study. The document mentions "Sample clinical images were taken for the hardware and software feature." This implies a set of images, but the quantity or characteristics of these images are not detailed.
- Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). The phrase "Sample clinical images were taken" suggests existing data, but further details are absent.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
- Not applicable/not provided in the context of this submission. The "image quality assessment" was performed by implicitly qualified personnel comparing images, but there is no mention of a formal "ground truth" establishment by multiple experts with specific qualifications to evaluate diagnostic accuracy metrics typically derived from AI output.
4. Adjudication Method for the Test Set
- Not applicable/not provided. No formal adjudication method like 2+1 or 3+1 is mentioned, as this is not a study assessing diagnostic accuracy outcomes from an AI.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and Effect Size
- No MRMC comparative effectiveness study was explicitly done to evaluate how human readers improve with AI vs. without AI assistance. The document refers to "MAGNETOM Lumina" as an MRI system, not an AI-assisted diagnostic tool for interpretation. The software feature (GOLiver) is for optimized image acquisition, minimizing user workflow in abdominal/liver MRI, not for diagnostic assistance to human readers.
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Not applicable. The MAGNETOM Lumina is an MRI device, which acquires images for a human to interpret. It is not a standalone algorithm meant to provide a diagnosis without human interaction.
7. The Type of Ground Truth Used
- For the "Image quality assessment of the new set of pulse sequences (GOLiver)," the "ground truth" implicitly referred to was a comparison against the image quality produced by the pulse sequences of the predicate device. This is a comparison of technical image characteristics rather than a clinical ground truth (e.g., pathology, surgical findings, long-term outcomes for disease presence).
8. The Sample Size for the Training Set
- Not applicable/not provided. The device is an MRI scanner. While there is software, the document doesn't describe an AI model that underwent "training" in the machine learning sense with a specific training set to learn diagnostic patterns. The "GOLiver" feature is described as "optimized pulse sequences," which implies engineering and parameter tuning, not machine learning model training.
9. How the Ground Truth for the Training Set Was Established
- Not applicable/not provided. As there's no mention of a traditional "training set" for an AI model, the concept of establishing ground truth for it is not relevant to this document.
§ 892.1000 Magnetic resonance diagnostic device.
(a)
Identification. A magnetic resonance diagnostic device is intended for general diagnostic use to present images which reflect the spatial distribution and/or magnetic resonance spectra which reflect frequency and distribution of nuclei exhibiting nuclear magnetic resonance. Other physical parameters derived from the images and/or spectra may also be produced. The device includes hydrogen-1 (proton) imaging, sodium-23 imaging, hydrogen-1 spectroscopy, phosphorus-31 spectroscopy, and chemical shift imaging (preserving simultaneous frequency and spatial information).(b)
Classification. Class II (special controls). A magnetic resonance imaging disposable kit intended for use with a magnetic resonance diagnostic device only is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.