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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 R5.7.1 SP4 MR Systems as compared to Philips legally marketed predicate device Ingenia Elition of the 510(k) submission Achieva, Intera, Ingenia, Ingenia CX, Ingenia Elition and Ingenia Ambition MR Systems (K193215, 04/10/2020).

In this 510(k) submission, Philips Medical Systems Nederland B.V. will be addressing the following minor software enhancements for the proposed Ingenia Elition R5.7.1 SP4 MR Systems:

1. Introduction of a persistence latch which will prevent the currently available Interlock of SmokeDetector to be reset by customer, which is against the current labeling (abnormal use).
2. The UI (User Interface) and IfU (Instructions for Use) are also updated to provide clear instructions for the user in case the currently available SmokeDetector Alarm Interlock is activated and to further clarify current instructions to prevent powercycling the system (abnormal use).
3. SW Compatibility with the PD-Break Heat Detector

Identical to the predicate device, the proposed Ingenia Elition R5.7.1 SP4 MR Systems is 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 document, a 510(k) Summary for Philips Medical Systems Nederland B.V.'s Ingenia Elition R5.7.1 SP4 MR Systems, focuses on demonstrating substantial equivalence to a predicate device. It addresses minor software enhancements, specifically related to a persistence latch for a SmokeDetector interlock, UI/IfU updates for the SmokeDetector alarm, and SW compatibility with a PD-Break Heat Detector.

Crucially, this document states that "The proposed Ingenia Elition R5.7.1 SP4 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." This implies that the device is not an AI/ML-powered medical device that would involve a study proving how AI-assistance improves human reader performance or the standalone performance of an AI algorithm.

Therefore, the requested information regarding acceptance criteria and a study proving the device meets those criteria, specifically concerning AI/ML performance, cannot be extracted from this document, as it describes a non-AI/ML device undergoing a Special 510(k) for minor software changes. The "acceptance criteria" discussed are related to compliance with consensus standards and general safety/performance, not AI/ML specific metrics.

However, based on the non-clinical performance data and the conclusion, we can infer the acceptance criteria for the software changes and how the device (with these changes) meets them.

Inferred Acceptance Criteria and Device Performance (for software changes)

Since the submission is a "Special 510(k)" for minor software enhancements, the acceptance criteria are primarily related to maintaining the safety and effectiveness of the existing, cleared device, and ensuring compliance with updated standards.

Information Not Applicable/Provided for an AI/ML Device

The following points are specifically requested for AI/ML-powered medical devices and are not applicable to this 510(k) summary, as it describes a conventional MR system with minor software updates.

2. Sample size used for the test set and the data provenance: Not applicable. No specific test set data for AI/ML performance is presented. The testing pertains to software verification and compliance with standards.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. No clinical ground truth establishment for AI/ML performance.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
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: Not applicable. This device is not AI-assisted.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an AI algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable.
8. The sample size for the training set: Not applicable. This is not an AI/ML device requiring a training set in the context of machine learning.
9. How the ground truth for the training set was established: Not applicable.

In summary, this 510(k) document indicates that the device is a Philips MR system with minor software changes and not an AI/ML-powered device. Therefore, the detailed AI/ML-specific study information requested is not present. The acceptance criteria and performance data are related to general device safety, effectiveness, and compliance with established medical device standards for MR systems.

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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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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 anatomy, the MR scan technique applied, and presence of contrast agents. The use of contrast agents for diagnostic 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. Images, spectra, and measurements of physical parameters, when interpreted by a trained physician, provide information that may assist 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.

The proposed MR 5300 and MR 7700 R11 MR Systems are 60 cm and 70 cm bore 1.5 and 3.0 Tesla (1.5T and 3.0T) Magnetic Resonance Diagnostic Devices, hereafter to be known as MR 5300 and MR 7700 MR Systems.

This Special 510(k) submission will include modifications of the proposed MR 5300 and MR 7700 R11 MR Systems as compared to our legally marketed devices, primary predicate device Achieva, Ingenia, Ingenia CX, Ingenia Elition and Ingenia Ambition MR Systems R11 (K213583, 05/16/2022) and the secondary predicate devices Ingenia 3.0T, Ingenia 3.0T CX, Ingenia Elition, and MR 7700 with distributed Multi Nuclei (K213516, 03/03/2022) and MR 5300 (K212673, 11/19/2021).

The proposed MR 5300 and MR 7700 MR systems will be brought up to the new baseline software R11. This R11 software is cleared on the Achieva, Ingenia, Ingenia CX, Ingenia Elition, and Ingenia Ambition MR Systems in the following primary predicate 510(k) Achieva, Ingenia, Ingenia CX, Ingenia Elition and Ingenia Ambition MR Systems R11 (K213583, 04/15/2022). Both the proposed MR 5300 and MR 7700 MR systems are already cleared with the secondary predicate devices Ingenia 3.0T, Ingenia 3.0T CX, Ingenia Elition, and MR 7700 with distributed Multi Nuclei (K213516, 03/03/2022) and MR 5300 (K212673, 11/19/2021).

Here's an analysis of the acceptance criteria and study information provided in the document:

1. Table of Acceptance Criteria and Reported Device Performance:

The document states: "The verification and/or validation test results demonstrate that the proposed MR 5300 and MR 7700 R11 MR Systems meet the acceptance criteria and are adequate for the intended use." and "The results of these tests demonstrate that the proposed MR 5300 and MR 7700 R11 MR Systems meet the acceptance criteria and are adequate for the intended use."

However, the provided text does not explicitly list specific acceptance criteria in a tabular format, nor does it quantify specific performance metrics for the device against such criteria. Instead, it refers to broad compliance with standards and successful completion of verification/validation tests. The device's performance is implicitly stated as "meeting the acceptance criteria" of these tests and compliance with recognized standards.

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

The document broadly mentions "Non-Clinical verification and or validation tests have been performed with regards to the intended use, the technical claims, the requirement specifications and the risk management results."

However, the text does not provide any details about the sample size (e.g., number of cases, number of images) used for these non-clinical verification and validation tests. It also does not specify the data provenance (e.g., country of origin, retrospective or prospective nature) of any data used in these tests.

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

The document makes no mention of experts being used to establish ground truth for a test set. The validation primarily focuses on technical compliance and functional verification against internal specifications and external standards.

4. Adjudication Method for the Test Set:

No information is provided regarding an adjudication method. This is consistent with the lack of expert involvement in establishing ground truth for a test set described in the document.

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

The document explicitly states: "The proposed Ingenia MR 5300 and MR 7700 R11 MR Systems did not require a clinical study since substantial equivalence to the legally marketed predicate device was proven with the verification/validation testing."

Therefore, no MRMC comparative effectiveness study was done, and consequently, no effect size of human readers improving with AI vs. without AI assistance is reported.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study:

The device is an MR System, a diagnostic imaging device. The various software enhancements (e.g., SmartSpeed AI, SmartSpeed MotionFree) listed are features of the MR system. The evaluation appears to be of the integrated system's technical and safety compliance rather than a standalone algorithm with distinct performance metrics evaluated without human intervention. The provided text does not describe a standalone algorithm-only performance study.

7. Type of Ground Truth Used:

Given that the document describes "Non-Clinical verification and or validation tests" and mentions compliance with "technical claims, the requirement specifications and the risk management results," the "ground truth" for the tests appears to be technical specifications, functional requirements, and established industry standards rather than expert consensus on medical images, pathology results, or outcomes data. The clearance is based on substantial equivalence to predicate devices demonstrated through these non-clinical tests.

8. Sample Size for the Training Set:

The document does not provide any information regarding a training set sample size. This is consistent with the nature of the submission, which focuses on hardware and software enhancements to existing medical devices rather than the development and validation of a new AI model requiring a separate training dataset. The AI functions mentioned (SmartSpeed AI) are integrated into the system, and their validation is part of the overall system's non-clinical testing.

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

As no training set is described, no information is provided on how ground truth for a training set was established.

Ask a specific question about this device

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 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 anatomy, the MR scan technique applied, and presence of contrast agents for diagnostic 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 assist 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 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.

The proposed Achieva, Ingenia, Ingenia CX, Ingenia Elition and Ingenia Ambition MR Systems R11.0 are 60 cm and 70 cm bore 1.5 and 3.0 Tesla (1.5T and 3.0T) Magnetic Resonance Diagnostic Devices, hereafter to be known as Achieva, Ingenia, Ingenia CX, Ingenia Elition and Ingenia Ambition MR Systems. This bundled abbreviated 510(k) submission will include modifications of the Achieva, Ingenia, Ingenia CX, Ingenia Elition and Ingenia Ambition MR Systems as compared to our legally marketed devices Achieva, Intera, Ingenia, Ingenia CX, Ingenia Elition and Ingenia Ambition MR Systems R5.7 (K193215, 04/10/2020). In this 510(k) submission, Philips Medical Systems Nederland B.V. will be addressing the following minor software enhancements to the proposed Achieva, Ingenia, Ingenia CX, Ingenia Elition and Ingenia Ambition MR Systems when compared to the legally marketed predicate Achieva, Intera, Ingenia, Ingenia CX, Ingenia Elition and Ingenia Ambition MR Systems R5.7 (K193215, 04/10/2020): 1. SmartSpeed AI 2. SmartSpeed MotionFree 3. SmartSpeed 3D FreeBreathing 4. SmartSpeed Implant 5. SmartSpeed DWI 6. MR Workspace 7. ISP MR Packages 8. Extended functionality Options This 510(k) submission will also address the following minor hardware enhancements: 1. Introduction of a graphical processing unit in the host recon computer for image reconstruction 2. Additional monitor as part of the operating console The supporting documentation provided for the proposed Achieva, Ingenia, Ingenia CX, Ingenia Elition and Ingenia Ambition MR Systems, includes software and hardware modifications that are addressed in test reports for system level development project, Voyager. The proposed Achieva, Ingenia, Ingenia CX, Ingenia Elition and Ingenia Ambition 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

The provided text describes modifications to Philips MR systems, specifically the integration of "SmartSpeed AI" which combines previously cleared Compressed-SENSE with machine learning for improved image acquisition. The document focuses on demonstrating substantial equivalence to a predicate device rather than outright proving a novel device's performance against clinical endpoints.

Here's an analysis of the acceptance criteria and study data based on the provided text:

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

The document doesn't explicitly present a formal "acceptance criteria table" with numerical targets. Instead, it describes the performance goals qualitatively, primarily focusing on "equivalent or better image quality" compared to images acquired without SmartSpeed AI but with longer scan times. The acceptance criteria essentially revolve around demonstrating that the new SmartSpeed AI feature does not negatively impact image quality and, ideally, improves it, especially at higher acceleration factors and lower SNR.

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

• Test Set Sample Size: The document does not specify a numerical sample size for the test set (number of images or patients). It mentions "a variety of datasets from different anatomies and image contrasts, varying SNR levels and acceleration factors" for pixel-wise comparison, and "in vivo images" for image quality analysis. For the reader evaluation study, it mentions "SmartSpeed AI images acquired across a variety of pulse sequences and anatomies."
• Data Provenance: Not explicitly stated. The phrase "in vivo images" and "a variety of datasets" implies real patient data, but the origin (e.g., country) is not mentioned, nor is whether the data was retrospective or prospectively collected for this study. Given it's a 510(k) for an upgrade, retrospective data is plausible.

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):

• Number of Experts: "A reader evaluation study with US board certified radiologists was performed." The exact number of radiologists is not specified, only that it was plural ("radiologists").
• Qualifications of Experts: "US board certified radiologists." No further details on their experience level (e.g., years of experience, subspecialty) are provided.

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

The document states: "Radiologists were asked to perform comparisons of SmartSpeed AI images acquired with shorter scan times and images without SmartSpeed AI acquired with longer scan times." It does not describe any specific adjudication method (e.g., consensus reading, majority vote) if there were multiple readers. It simply states "The combined results of the comparison described above confirmed..." suggesting an aggregation of individual reader opinions.

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:

• MRMC Study: A reader evaluation study was performed, which is a type of MRMC study. However, this study was not designed to measure "how much human readers improve with AI vs. without AI assistance." Instead, it was designed to compare the image quality of SmartSpeed AI images (shorter scan time) against non-SmartSpeed AI images (longer scan time), with human readers providing the comparison. The goal was to show non-inferiority or superiority in image quality, not an improvement in diagnostic performance of the human reader.
• Effect Size: No effect size regarding human reader improvement is reported because that was not the objective of the study. The study aimed to assess equivalent or better image quality of the AI-processed images.

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

Yes, a standalone performance assessment was done. The document states:

• "A pixel-wise comparison was performed to confirm that SmartSpeed AI does provide comparable or better results than the data reconstructed without SmartSpeed AI."
• "SmartSpeed AI showed better alignment with the ground truth data for high acceleration factors and low SNR levels compared to the data reconstructed without SmartSpeed AI."
• "In vivo images were analyzed to confirm that SmartSpeed AI does not negatively impact image quality measures when acquired with reduced scan time."

These directly assess the algorithm's output (image quality) without human interpretation in the loop as the primary endpoint for these specific analyses.

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

The ground truth for the pixel-wise comparison was "fully sampled ground truth data." This implies a reference image acquired with conventional, unaccelerated scanning techniques, which is considered the "true" or ideal image without any AI reconstruction. For the in vivo image quality assessment and reader study, the ground truth was essentially the "image quality" as perceived by "US board certified radiologists" in comparison to the non-AI enhanced, longer-scan-time images. It's a comparative ground truth based on expert perception rather than a definitive clinical diagnosis or pathology.

8. The sample size for the training set:

The document does not specify the sample size for the training set used for the "SmartSpeed AI" machine learning component.

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

The document mentions that SmartSpeed AI "combining the previously cleared and legally marketed feature Compressed-SENSE... with machine learning." Given the context of image reconstruction and enhancement, it's highly probable the training ground truth involved pairs of unaccelerated (or conventionally accelerated) MR images and corresponding undersampled or noisy MR data, allowing the AI to learn to reconstruct high-quality images from suboptimal inputs. However, the exact method for establishing this ground truth (e.g., specific image acquisition protocols, expert annotation for quality metrics) is not detailed in the provided text.

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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 anatomy, the MR scan technique applied, and presence of contrast agents. The use of contrast agents for diagnostic 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 assist 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.

The proposed Ingenia 3.0T, Ingenia 3.0T CX, Ingenia Elition and MR 7700 with Distributed Multi Nuclei R5.9 are provided on the 60 cm and 70 cm bore 3.0 Tesla (3.0T) Magnetic Resonance Diagnostic Devices.

This bundled abbreviated 510(k) submission will include modifications of the 3.0T systems, included in the legally marketed predicate device Achieva, Intera, Ingenia, Ingenia CX, Ingenia Elition, and Ingenia Ambition MR Systems R5.7 (K193215, 04/10/2020).

This 510(k) submission will address the following HW and SW modifications for the proposed Ingenia 3.0T, Ingenia 3.0T CX, Ingenia Elition and MR 7700 with Distributed Multi Nuclei R5.9 since the clearance of the last submission for each of the systems: New system MR 7700 which contains modified gradient system compared to Ingenia Elition X Modified Multi Nuclei option, now available for all 3.0T systems

This 510(k) submission will also address minor hardware and software enhancements: Universal Mains Distribution Unit (uMDU) 3.0T 1H RF Amplifier Extended Functionality Options

The proposed Ingenia 3.0T, Ingenia 3.0T CX, Ingenia Elition and MR 7700 with Distributed Multi Nuclei R5.9 are intended to be marketed with the following pulse sequences and coils that were previously cleared by FDA: mDIXON (K102344) SWIp (K131241) mDIXON-Quant (K133526) mDIXON XD (K143128) O-MAR K143253 3D APT (K172920) Coils compatible with Ingenia 3.0T, Ingenia 3,0T CX, Ingenia Elition and MR 7700

The proposed Ingenia 3.0T, Ingenia 3.0T CX, Ingenia Elition and MR 7700 with Distributed Multi Nuclei R5.9 are substantially equivalent to the legally marketed predicate device Achieva, Intera, Ingenia, Ingenia CX, Ingenia Elition, and Ingenia Ambition MR Systems R5.7 (K193215, 04/10/2020).

The provided text describes modifications to existing Philips Magnetic Resonance (MR) systems (Ingenia 3.0T, Ingenia 3.0T CX, Ingenia Elition, and MR 7700) with the addition of "Distributed Multi Nuclei" functionality. The submission argues for substantial equivalence to a legally marketed predicate device (Achieva, Intera, Ingenia, Ingenia CX, Ingenia Elition, and Ingenia Ambition MR Systems R5.7, K193215).

However, the document does not describe a study that proves the device meets specific performance acceptance criteria in the way one might expect for a diagnostic AI device (e.g., sensitivity, specificity, AUC). Instead, it relies on demonstrating substantial equivalence through non-clinical verification and validation testing, and compliance with recognized standards.

Here's an analysis based on the information provided, highlighting what is present and what is missing concerning your request:

1. Table of Acceptance Criteria and Reported Device Performance

This information is not provided in the document as a quantitative table of performance metrics. The document states: "Test results demonstrate that the proposed Ingenia 3.0T, Ingenia 3.0T CX, Ingenia Elition and MR 7700 with Distributed Multi Nuclei R5.9 meet the acceptance criteria and are adequate for its intended use." However, it does not specify what those acceptance criteria are nor what the reported performance values against those criteria are for the functional enhancements (Modified Gradient System, Distributed Multi Nuclei, uMDU, 3.0T 1H RF Amplifier).

The "acceptance criteria" appear to be related to compliance with international, FDA recognized consensus standards, and the intended use of the MR system as a diagnostic device. The performance is implied to be equivalent to the predicate device.

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

This information is not applicable in the context of a clinical study for a diagnostic algorithm. The document describes "non-clinical verification and/or validation tests." These would typically involve engineering tests, phantom studies, and system-level performance checks rather than a test set of patient data with ground truth for diagnostic accuracy.

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

This information is not applicable. Since no clinical study or diagnostic accuracy study with a "test set" in the context of expert-established ground truth is described, this detail is not present. The device enables physicians to obtain images and spectra; the interpretation by a "trained physician" is mentioned as part of the intended use, but not as part of a ground truth establishment process for the device's own performance evaluation.

4. Adjudication Method for the Test Set

This information is not applicable for the same reasons as points 2 and 3.

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. The document explicitly states: "The proposed Ingenia 3.0T, Ingenia 3.0T CX, Ingenia Elition and MR 7700 with Distributed Multi Nuclei R5.9 did not require a clinical study since substantial equivalence to the legally marketed predicate device was proven with the verification/validation testing." Therefore, no MRMC study or AI assistance effect size is mentioned.

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

This information is not applicable. The device is an MR imaging system itself, not a standalone diagnostic algorithm that would typically undergo such testing. Its function is to acquire images and spectra for human interpretation.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

This information is not applicable in the context of evaluating diagnostic accuracy using a test set of patient data. The "ground truth" for the non-clinical tests would have been established through engineering specifications, physical measurements, and compliance with recognized standards for MR system performance.

8. The Sample Size for the Training Set

This information is not applicable. The document does not describe an AI or machine learning algorithm that requires a training set in the conventional sense for diagnostic classification. The modifications are to the physical and software components of an MR system for image acquisition and processing capabilities.

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

This information is not applicable for the same reasons as point 8.

Summary of what the document does provide regarding acceptance criteria and proof:

The document establishes "substantial equivalence" to a predicate device (K193215) by demonstrating that the modifications (Modified Gradient System, Distributed Multi Nuclei, uMDU, 3.0T 1H RF Amplifier, and minor software enhancements) do not raise different questions of safety and effectiveness, and that the device continues to meet its intended use.

The proof described is through:

• Non-clinical verification and validation tests: These tests were performed "with regards to the intended use, the technical claims, the requirement specifications and the risk management results."
• Compliance with international and FDA recognized consensus standards: A list of standards (e.g., IEC60601 series, IEC62366-1, IEC 62304, NEMA MS series, ISO 14971, and various FDA guidance documents) is provided.
• Risk management activities: To ensure all identified risks are sufficiently mitigated and overall residual risk is acceptable.

The acceptance criteria implicitly refer to successfully passing these non-clinical tests, complying with all listed standards, adequately mitigating risks, and maintaining the same safety and effectiveness profile as the predicate device such that no new clinical study was deemed necessary. The "reported device performance" is the successful fulfillment of these criteria, leading to the conclusion of substantial equivalence.

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