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Philips Magnetic Resonance (MR) systems are Medical Electrical Systems indicated for use as a diagnostic device. This MR system enables trained physicians to obtain cross-sectional images, spectroscopic images and/or spectra of the internal structure of the head, body or extremities, in any orientation, representing the spatial distribution of protons or other nuclei with spin. Image appearance is determined by many different physical properties of the tissue and the MR scan technique applied, and presence of contrast agents. The use of contrast imaging applications should be performed consistent with the approved labeling for the contrast agent. The trained clinical user can adjust the MR scan parameters to customize image appearance, accelerate image acquisition, and synchronize with the patient's breathing or cardiac cycle. The systems can use combinations of images to produce physical parameters, and related derived images, spectra, and measurements of physical parameters, when interpreted by a trained physician, provide information that may assust diagnosis and therapy planning. The accuracy of determined physical parameters depends on system and scan parameters, and must be controlled and validated by the clinical user. In addition the Philips MR systems provide imaging capabilities, such as MR fluoroscopy, to guide and evaluate interventional and minimally invasive procedures in the head, body and extremities. MR Interventional procedures, performed inside or adjacent to the Philips MR system, must be performed with MR Conditional or MR Safe instrumentation as selected and evaluated by the clinical user for use with the specific MR system configuration in the hospital. The appropriateness and use of information from a Philips MR system for a specific interventional procedure and specific MR system configuration must be validated by the clinical user.

This Special 510(k) submission will include modifications of the proposed Ingenia Elition and MR 7700 MR Systems as compared to Philips legally marketed devices, primary predicate device MR 7700 R11 MR System of the 510(k) submission MR 5300 and MR 7700 R11 MR Systems (K223442, 12/23/2022) as well as the secondary predicate device being the legally marketed Ingenia Elition R11 MR System of the 510(k) submission Achieva, Ingenia, Ingenia CX, Ingenia Elition and Ingenia Ambition MR Systems R11 (K213583, 04/15/2022). In this 510(k) submission, Philips Medical Systems Nederland B.V. will be addressing the following minor hardware enhancements for the proposed Ingenia Elition and MR 7700 MR Systems since the last 510(k) submission (K223442, 12/23/2022) for each of the systems: 1. The SmokeDetector Interlock, a component used in the legally marketed Ingenia Elition and MR 7700 systems, becomes a mandatory risk control measure. 2. Minor design change to current gradient coil type WB30S Identical to the predicate devices, the proposed Ingenia Elition and MR 7700 MR Systems are intended to be marketed with the following pulse sequences and coils that are previously cleared by FDA: 1. mDIXON (K102344) 2. SWIp (K131241) 3. mDIXON-Quant (K133526) 4. MRE (K140666) 5. mDIXON XD (K143128) 6. O-MAR (K143253) 7. 3D APT (K172920) 8. Compatible System Coils (identical to the predicate devices)

The provided FDA 510(k) summary for the Philips Ingenia Elition and MR 7700 MR Systems does not contain information about acceptance criteria or a study that proves the device meets specific performance criteria in the context of an AI/human-in-the-loop study. Instead, this submission is for minor hardware enhancements and focuses on demonstrating substantial equivalence to previously cleared predicate devices through compliance with recognized standards and non-clinical verification tests.

Therefore, I cannot provide details for most of the requested information points, as they are not present in the given text. The submission explicitly states: "The proposed Ingenia Elition and MR 7700 MR Systems did not introduce any modification to the indication for use or technological characteristics relative to the predicate devices that would require clinical testing."

However, I can extract information related to the non-clinical performance data and the general approach.

Here's the breakdown of what can be inferred and what cannot:

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

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Not provided. The submission focuses on non-clinical verification tests against standards for hardware modifications, not performance on a specific dataset.

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 applicable/provided. No clinical study involving expert ground truth is described. The assessment is based on engineering verification and compliance with standards.

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

• Not applicable/provided. No clinical study 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, not done. This submission is for minor hardware enhancements to an MR system, not for an AI-powered diagnostic aid, and therefore, no MRMC study or AI assistance evaluation is mentioned.

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

• No, not done. As mentioned, this is not an AI-powered device.

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

• Not applicable/provided. For the purpose of this submission, the "ground truth" for the device's acceptable performance is defined by compliance with established international and FDA-recognized consensus standards for medical electrical equipment and risk management.

8. The sample size for the training set

• Not applicable/provided. There is no mention of a training set as this is not an AI/machine learning device submission.

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

• Not applicable/provided. There is no mention of a training set.

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