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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 subject Ingenia, Ingenia CX, Ingenia Elition, Ingenia Ambition, BlueSeal, MR 5300 and MR 7700 MR Systems are 60 cm and 70 cm bore 1.5 and 3.0 Tesla (1.5T and 3.0T) Magnetic Resonance Diagnostic Devices.

A new optional software feature SmartSpeed Precise is contained in software R12.3 for the subject Ingenia, Ingenia CX, Ingenia Elition, Ingenia Ambition, BlueSeal, MR 5300 and MR 7700 MR Systems.

SmartSpeed Precise is a machine learning based reconstruction technique designed to increase the signal-to-noise (SNR), increase the sharpness, and reduce residual ringing artifacts in MR images.

The introduction of SmartSpeed Precise only required updates to the MR System Software.

The subject Ingenia, Ingenia CX, Ingenia Elition, Ingenia Ambition, BlueSeal, MR 5300 and MR 7700 MR Systems are intended to be marketed with the same pulse sequences and coils that are previously cleared by FDA:

The accessories to be used with the subject Ingenia, Ingenia CX, Ingenia Elition, Ingenia Ambition, BlueSeal, MR 5300 and MR 7700 MR Systems have not changed compared to the predicate device and can be found in the Instructions for Use accompanying the device.

When Philips MRI system is used in combination with the Philips MR-RT or MR-OR solutions, the user is referred to the dedicated MR-RT and MR-OR Instructions for Use for information on additional accessories that may apply.

This FDA 510(k) clearance letter describes the acceptance criteria and study proving the Philips MR systems meet these criteria, particularly focusing on the new SmartSpeed Precise feature.

Here's an breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The FDA clearance letter does not provide a specific quantitative table of acceptance criteria with corresponding numerical performance metrics for the SmartSpeed Precise feature. Instead, it describes the acceptance criteria implicitly through the study design and the qualitative assessments.

The core acceptance is that the new SmartSpeed Precise feature, as implemented in the Philips MR Systems, maintains at least equivalent image quality for diagnostic purposes while providing benefits like increased SNR, sharpness, and reduced ringing artifacts, and does not negatively impact safety or effectiveness compared to the predicate device.

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

The document mentions "a reader evaluation by ABR board certified radiologists" was performed, and "qualitative image review" by application specialists. However, the specific sample size (number of cases/images) used for these evaluations (testing) is not explicitly stated in the provided text.

The data provenance is not specified regarding country of origin. It is described as non-clinical performance data, suggesting it's likely retrospective data (existing images) or phantom/simulated data, rather than prospective patient recruitment solely for this study. The phrasing "Simulated use validation was executed" further supports this.

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

• Experts for Qualitative Image Review: "application specialists" performed the qualitative review. Their specific number and detailed qualifications (e.g., years of experience, certifications beyond "application specialist") are not specified.
• Experts for Reader Evaluation: "ABR board certified radiologists" performed the reader evaluation. The number of radiologists is not specified. Their qualification is clearly stated as "ABR board certified radiologists."

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1) for resolving disagreements among readers. It states "a reader evaluation by ABR board certified radiologists was performed" and "the review evaluation shows that the proposed device is assessed as equivalent." This suggests a consensus or majority opinion might have been reached amongst them, but the process is not detailed.

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

Yes, a multi-reader multi-case comparative effectiveness study appears to have been done implicitly as part of the "reader evaluation by ABR board certified radiologists."

• Effect Size of Human Readers' Improvement with AI vs. Without AI Assistance:
  The study was designed to compare the new SmartSpeed Precise (AI-enhanced reconstruction) against the predicate device's reconstruction technology. The document states:
  "The review evaluation shows that the proposed device is assessed as equivalent for diagnosis and holds significantly better SNR and sharpness compared to the predicate reconstruction technology, also in the presence of (subtle) abnormalities and pathology."
  This indicates a positive effect size on image quality metrics (SNR, sharpness) directly impacting the interpretability for human readers. However, specific numerical effect sizes (e.g., mean difference in reader performance metrics, confidence intervals, p-values) are not provided in this summary. The primary claim is "equivalent for diagnosis" but with "significantly better SNR and sharpness."

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

The description focuses on the impact of SmartSpeed Precise on image quality for human interpretation. While a machine learning model is central to SmartSpeed Precise, the assessment is not described as a purely standalone (algorithm-only) performance evaluation independent of human interpretation. The features explicitly assessed (SNR, sharpness, CNR, artifact level) are objective image quality metrics that could be evaluated standalone, but their context in this document is in relation to human reader assessment ("radiologists have assessed the quality of the visualization of abnormalities...").

7. Type of Ground Truth Used

The ground truth for the reader evaluation appears to be based on:

• Expert Consensus/Judgment: The "ABR board certified radiologists" assessed image properties (SNR, sharpness, CNR, artifacts) and the "quality of the visualization of abnormalities and pathologies" and whether images were "of sufficient quality for diagnostic purposes." This implies their collective professional judgment formed the ground truth for diagnostic equivalence and quality assessment.
• Implicit Clinical Ground Truth: For cases with abnormalities/pathologies, the underlying clinical diagnosis (presumed to be established through other means like prior imaging, clinical follow-up, or pathology reports) would serve as the ultimate ground truth against which the visualization of these abnormalities was assessed by the radiologists. However, the specific method for establishing this clinical ground truth for the cases themselves is not detailed.

8. Sample Size for the Training Set

The document does not specify the sample size used for training the SmartSpeed Precise machine learning model. It only states that "SmartSpeed Precise is a machine learning based reconstruction technique."

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

The document does not specify how the ground truth for the training set was established. Given that it's a "machine learning based reconstruction technique" focused on improving SNR, sharpness, and reducing artifacts, the training ground truth would typically involve pairs of noisy/artifact-ridden input images and corresponding "ideal" or "truth" images (e.g., acquired with very long scan times or gold-standard techniques not feasible in routine clinical practice) against which the model learns to reconstruct improved images. However, the exact methodology is not described.

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