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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 Achieva, Intera, Ingenia 1.5T, Ingenia 3.0T, Ingenia 1.5T CX, Ingenia 3.0T CX, Ingenia Elition S, Ingenia Elition X, Ingenia Ambition S, Ingenia Ambition X and MR 5300 MR Systems are 60 cm and 70 cm bore 1.5 and 3.0 Tesla (1.5T and 3.0T) Magnetic Resonance Diagnostic Devices.
In this 510(k) submission, Philips Medical Systems Netherlands B.V. will be addressing the following software changes for the subject device since the last 510(k) submission (primary predicate device (K212673, 11/19/2021), secondary predicate device (K193215, 04/10/2020)):

• Software functionality that allows early detection of severe gradient malfunctions. The system is locked for scanning if a malfunction is detected.
• Smoke detector software support extension from Ingenia Elition S and X to all other 70cm bore systems
• SENSE XL Torso Coil workflow extensions to guide the operator on safe usage of the SENSE XL Torso Coil and monitoring of the SENSE XL Torso Coil temperature

The subject Achieva, Intera, Ingenia 1.5T, Ingenia 3.0T, Ingenia 1.5T CX, Ingenia 3.0T CX, Ingenia Elition S, Ingenia Elition X, Ingenia Ambition S, Ingenia Ambition X and MR 5300 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 devices have not changed compared to the predicate 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.

The provided FDA 510(k) clearance letter and its associated 510(k) Summary for Philips MR Systems (K251808) DO NOT contain the detailed information required to describe the acceptance criteria and the study that proves the device meets the acceptance criteria in the manner requested.

This submission is a Special 510(k), which is used for modifications to a manufacturer's own legally marketed device where the modified device is still substantially equivalent to the cleared device, and the modifications do not raise new questions of safety and effectiveness. This type of submission often relies heavily on risk management and performance testing to demonstrate that the changes have not degraded the safety or effectiveness of the device.

The document primarily focuses on software changes related to safety-critical functions (gradient malfunction detection, smoke detector interlock, SENSE XL Torso Coil temperature monitoring) and asserts substantial equivalence to existing predicate devices.

Here's why the requested information cannot be fully extracted and what can be inferred:

Summary of Information NOT Present in the Document:

• Detailed Acceptance Criteria Table with Performance Data: The document states that "The verification and/or validation test results demonstrate that the subject... MR Systems meet the acceptance criteria and are adequate for the intended use" (Page 8), but it does not provide a specific table of these acceptance criteria nor the numerical performance data for the device against them. The information provided about the software changes focuses on their functionality and equivalence rather than quantitative performance metrics (e.g., specific thresholds for gradient malfunction detection sensitivity/specificity, or precise temperature monitoring accuracy).
• Sample Size for Test Set and Data Provenance: No specific test set sample sizes (e.g., number of patients, number of scans) are mentioned. The testing described is "Non-Clinical verification and validation tests," implying it's not based on patient data in the typical sense for clinical performance.
• Number of Experts and Qualifications for Ground Truth: Since no clinical data or diagnostic performance studies are described (only non-clinical V&V), there's no mention of experts establishing ground truth for a test set.
• Adjudication Method for Test Set: Not applicable as no clinical study with expert reads is described.
• Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study: Not mentioned as no clinical study evaluating human reader performance is described.
• Standalone (Algorithm Only) Performance: While the software features operate automatically, no quantitative standalone performance data (e.g., TPR, FPR for malfunction detection) is provided. The focus is on the functionality of the safety mechanisms (e.g., "system is locked," "enforces a cool down period").
• Type of Ground Truth Used: For the safety software functions, the "ground truth" would likely be derived from engineering specifications, simulated fault conditions, or direct sensor readings validated against known physical parameters, rather than expert consensus on medical images or pathology. The document hints at this by stating "verification and/or validation test results demonstrate that the subject... meet the acceptance criteria and are adequate for the intended use."
• Sample Size for Training Set & Ground Truth Establishment for Training Set: Since these are modifications to existing, cleared devices, and the described changes are primarily safety and workflow enhancements, it's unlikely that new deep learning models requiring large imaging training sets were developed for these specific features. If machine learning was involved in the gradient malfunction detection, the specific training methodology is not disclosed. The document implies engineering-based verification and validation rather than large-scale data-driven model training.

Information that CAN be Inferred or Directly Stated:

• Study Type: Non-clinical verification and validation tests, adhering to recognized international and FDA-recognized consensus standards (e.g., IEC 60601-1-6 for usability, IEC 62304 for software lifecycle, ISO 14971 for risk management).
• Overall Conclusion: The non-clinical testing demonstrates that the modified MR Systems "meet the acceptance criteria and are adequate for the intended use" and that "all risks are sufficiently mitigated, that no new risks are introduced, and that the overall residual risks are acceptable." This leads to the conclusion of substantial equivalence.
• Basis for Ground Truth (Implied): For the safety features, the "ground truth" is established by engineering specifications, simulated fault conditions, and internal testing to ensure the alarm/lockout mechanisms activate correctly under defined hazardous conditions and that the temperature monitoring is accurate.
• Key Software Changes and Their Demonstrated Functionality:
  • Severe Gradient Malfunction Detection: Modifies the system to lock when the frequency of gradient amplifier errors exceeds predefined thresholds, preventing severe malfunctions. The previous system only aborted individual errors and allowed continuation. The "study" here would be demonstrating that the system properly locks when these new thresholds are met and does not falsely lock otherwise.
  • Smoke Detector Software Support Extension: Extends existing 60cm MR System smoke detector interlock software to 70cm MR Systems. The "study" would show that the same logic and safety performance are maintained on the new platform.
  • SENSE XL Torso Coil Workflow Extensions: Software now enforces a cool-down period for the coil and guides the operator on temperature management. The "study" likely involved testing the logic of the cool-down enforcement and the accuracy/timing of the temperature warnings.

Based on the provided document, here's the most that can be extracted and inferred about the acceptance criteria and studies:

Device: Philips Achieva, Intera, Ingenia 1.5T, Ingenia 3.0T, Ingenia 1.5T CX, Ingenia 3.0T CX, Ingenia Elition S, Ingenia Elition X, Ingenia Ambition S, Ingenia Ambition X and MR 5300 MR Systems (software modifications).

Purpose of the Study: To demonstrate that software changes implemented for gradient malfunction detection, smoke detector interlock extension, and SENSE XL Torso Coil temperature monitoring do not raise new questions of safety and effectiveness and that the modified devices remain substantially equivalent to their predicate devices.

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample Size Used for the Test Set and Data Provenance:
The document states "Non-Clinical verification and validation tests have been performed." This implies engineering-level testing rather than patient-based clinical studies. No specific sample sizes (e.g., number of scan instances, number of patients) are provided. The data provenance is internal Philips testing, likely performed in the Netherlands (Philips Medical Systems Nederland B.V.). It is retrospective in the sense that it evaluates modifications to existing systems.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications:
Not applicable. Ground truth for these safety software functions is based on engineering specifications and direct measurement/simulation of system states and physical parameters (e.g., gradient performance, temperature, smoke detection), not on expert interpretations of medical images.

4. Adjudication Method for the Test Set:
Not applicable. There is no mention of human readers or adjudication processes for clinical image interpretation.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:
No, an MRMC comparative effectiveness study was not done or reported in this 510(k) summary. The nature of these software modifications (safety and workflow enhancements) does not typically necessitate such a study for 510(k) clearance, especially in a Special 510(k). The document explicitly states "the indications for use remain unchanged and there were no technological characteristics relative to the predicate device that would require clinical testing."

6. If a Standalone (Algorithm Only) Performance was done:
Yes, in the sense that the software features (e.g., gradient malfunction detection, temperature monitoring logic) are algorithms operating on system data. However, quantitative performance metrics (e.g., sensitivity, specificity, accuracy) for these algorithms are not explicitly provided in the summary. The "performance" described is functional—that the system does lock, does enforce cool-downs, etc., when conditions are met.

7. The Type of Ground Truth Used:
For the software changes described, the ground truth is primarily based on:
* Engineering Specifications: Defining thresholds for gradient errors, acceptable temperature ranges for the coil.
* Simulated Fault Conditions: Testing whether the gradient malfunction detection activates correctly under controlled, simulated error rates or conditions.
* Direct Physical Measurements: Verifying the accuracy of temperature sensors and the initiation of cool-down periods.
* Compliance with Standards: Ensuring the robust implementation of software safety features as per recognized industry standards.

8. The Sample Size for the Training Set:
The document does not specify a training set sample size. Given the nature of the software changes (safety logic and workflow enhancements) and this being a Special 510(k), it is unlikely to involve large-scale machine learning model training on medical image datasets. If any internal parameters were 'trained' (e.g., for optimal gradient error thresholds), the details are not disclosed.

9. How the Ground Truth for the Training Set Was Established:
Not explicitly stated. If there were any trainable parameters for the safety functions, the ground truth would have been established via engineering analysis, simulation, and empirical testing to define the optimal setpoints for safety interlocks and warnings.

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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 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 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 Achieva, Intera, Ingenia, Ingenia CX, Ingenia Elition, and Ingenia Ambition MR Systems R5.7 with MultiBand SENSE software feature are provided on the 60 cm and 70 cm bore 1.5 Tesla (1.5T) and 3.0 Tesla (3.0T) Magnetic Resonance Diagnostic Devices.

Hereafter Achieva, Intera, Ingenia, Ingenia CX, Ingenia Elition, and Ingenia Ambition MR Systems R5.7 with MultiBand SENSE software feature will be referred to as the proposed Achieva, Intera, Ingenia, Ingenia CX, Ingenia Elition, and Ingenia Ambition MR Systems in this submission.

This bundled abbreviated 510(k) submission will include software modifications to the following legally marketed MR systems: Ingenia 1.5T, Ingenia 1.5T S, Ingenia 3.0T, Ingenia 1.5T CX, Ingenia 3.0T CX, Ingenia Elition S, Ingenia Elition X, Ingenia Ambition S and Ingenia Ambition X (K183063, 02/14/2019) and Achieva 1.5T, Achieva 3.0T, Intera 1.5T (K190461, 06/04/2019).

All of the aforementioned legally marketed systems will be brought up to the new baseline software R5.7. This submission addresses only software modifications, there are no hardware modifications made to any of the above legally marketed systems.

In this 510(k) submission, Philips Medical Systems Nederland B.V. will be addressing modifications to MultiBand SENSE and one labeling change to the proposed Achieva, Intera, Ingenia, Ingenia CX, Ingenia Elition and Ingenia Ambition MR Systems:

• Removal of contra-indication statement of Compressed SENSE with Gd contrast agent

This 510(k) submission will also address minor software enhancements contained in software R5.7 for the proposed Achieva, Intera, Ingenia, Ingenia CX, Ingenia Elition, and Ingenia Ambition MR Systems since the clearance of the last submission for each of the systems:

1. 4D FreeBreathing
2. MR Elastography Extension
3. EPIC Brain
4. LOVA ADC
5. Computed DWI
6. SmartShim
7. VitalScreen
8. Extended Functionality Options

The proposed Ingenia, Ingenia CX, Ingenia Elition, and Ingenia Ambition MR Systems are intended to be marketed with the following pulse sequences and coils that were previously cleared by FDA:

1. mDIXON (K102344)
2. SWIp (K131241)
3. mDIXON-Quant (K133526)
4. mDIXON XD (K143128)
5. O-MAR K143253
6. 3D APT (K172920)
7. Ingenia Coils

The proposed Achieva, Intera, Ingenia, Ingenia CX, Ingenia Elition and Ingenia Ambition MR Systems are substantially equivalent to the legally marketed predicate device Ingenia, Ingenia CX, Ingenia Elition, and Ingenia Ambition MR Systems (K183063, 02/14/2019).

In addition, the proposed Achieva, Intera, Ingenia, Ingenia CX, Ingenia Elition and Ingenia Ambition MR Systems is substantially equivalent to the following legally marketed reference devices: MultiBand SENSE software application (K162940, 12/30/2016), to support MultiBand SENSE for 1.5T and to support diffusion body imaging on 1.5T and 3.0T.Ingenia 1.5T, Ingenia 1.5T S, Ingenia 1.5T CX, Ingenia 3.0T CX, and Ingenia 3.0T CX R5.4 K173079, 04/04/2018, to support the removal of the contra-indication of the compatibility of Compressed SENSE with (dynamic) Gadolinium contrast-enhanced imaging.

The provided text is a 510(k) Summary for Philips MR Systems, primarily addressing software modifications and enhancements. It focuses on demonstrating substantial equivalence to previously cleared devices rather than presenting a novel AI/CAD device. Therefore, much of the requested information regarding acceptance criteria specifically for AI/CAD performance, MRMC studies, and detailed ground truth establishment for a test set as typically seen for AI algorithm validation is not explicitly detailed in this document.

However, based on the non-clinical performance data section, we can infer some information about "acceptance criteria" in the context of demonstrating equivalence for the software modifications.

Here's an attempt to extract and present the information based on the provided text:

Device: Achieva, Intera, Ingenia, Ingenia CX, Ingenia Elition, and Ingenia Ambition MR Systems (with software modifications R5.7, including MultiBand SENSE and Compressed SENSE with contrast among other enhancements).

Study Goal: To demonstrate substantial equivalence of the modified MR systems to legally marketed predicate devices, particularly regarding the performance of the software features.

Acceptance Criteria and Reported Device Performance

The document doesn't present a specific table of acceptance criteria with numerical performance targets typical for AI/CAD devices (e.g., sensitivity, specificity, AUC). Instead, the acceptance criteria are implicitly performance characteristics demonstrating equivalence to the predicate device and proper functioning of the new features.

Study Details

1. Sample sizes used for the test set and the data provenance:

   • For Compressed SENSE with Contrast:
     • "Bench test results (Shelley phantom)" - number of phantom acquisitions not specified.
     • "Retrospectively and prospectively sub-sampled data" - not specified if this refers to phantom or human data for prospectively sub-sampled.
     • "Data from retrospective sub-sampled CE-angio data, on 3 human subjects."
     • "Clinical data for brain perfusion in tumor classification was provided." (Number of subjects not specified, but this refers to another retrospective subsampling approach.)
   • For other features, the document states "Non-Clinical verification and or validation tests have been performed on all of the software modifications" but does not specify sample sizes for test images or subjects.
   • Data Provenance: Not explicitly stated regarding country of origin. The data appears to be retrospective (e.g., "retrospective sub-sampled CE-angio data"). It's a non-clinical submission, so no large-scale clinical trial data is expected.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • No information provided regarding experts establishing ground truth for a test set. This submission focuses on demonstrating substantial equivalence based on technical and performance characteristics of the MR sequences, not on diagnostic accuracy of an AI interpreting images for specific conditions. The "ground truth" here is implied to be the established performance of the predicate device (non-accelerated acquisition) or physical properties measured by phantoms.
   • The document implies that "interpreted by a trained physician" is crucial for clinical use, but this is not about ground truth for the device's performance testing.

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

   • Not applicable/Not specified. This type of submission does not detail an adjudication process for a diagnostic interpretative task.

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:

   • No. An MRMC comparative effectiveness study was not performed and is not described in this 510(k) summary. This submission pertains to modifications of the MR scanner's acquisition capabilities, not an AI or CAD system that assists human readers with diagnostic interpretation.

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

   • The studies mentioned (bench tests, subsampled data comparisons) were essentially "standalone" evaluations of the image reconstruction algorithms' technical performance (e.g., temporal and spatial resolution, contrast appearance, robustness) against a reference (fully sampled or non-accelerated data). No specific metrics like sensitivity/specificity for a diagnostic task are provided, as the device is the scanner itself, not an interpretative AI.

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

   • The "ground truth" for the technical performance evaluations appears to be:
     • Phantom data: For assessing properties like time-intensity behavior (Shelley phantom).
     • Fully sampled (non-accelerated) MR acquisition data: Used as a reference for comparison with accelerated acquisition (Compressed SENSE) to assess image quality, spatial/temporal resolution, and contrast appearance.
     • Implied clinical utility/diagnostic information: The "clinical data for brain perfusion in tumor classification" might have relied on clinical diagnosis or other established medical information for the "tumor classification" aspect, but the primary comparison was the technical quality of the MR images generated by the new sequence versus the predicate.

7. The sample size for the training set:

   • Not applicable/Not specified. This document describes improvements to existing MR sequences and software, not a de novo AI model that requires a distinct "training set." The development of the algorithms would have involved internal testing and validation, but not in the sense of a machine learning training dataset for a specific diagnostic task from a large, labeled dataset.

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

   • Not applicable, as no external "training set" in the context of machine learning model development is described.

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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 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 R 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 submission covers the proposed Achieva 1.5T, Achieva 3.0T, and the Intera 1.5T MR Systems R5.6, hereafter to be known as Achieva 1.5T, 3.0T, and Intera 1.5T MR Systems. The proposed Achieva 1.5T, 3.0T, and Intera 1.5T MR Systems are 60 cm bore 1.5 Tesla (1.5T) and 3.0 Tesla (3.0T) Magnetic Resonance Diagnostic Devices. This submission of the proposed Achieva 1.5T, 3.0T, and Intera 1.5T MR Systems contains a description of the software and hardware modifications made since the last 510(k) clearance of the primary predicate Achieva, Intera & Panorama 1.0T R2.5 (K063559, 01/04/2007). The Achieva 1.5T and Intera 1.5T systems differ in outside covers only, both systems function in an identical manner. The proposed Achieva 1.5T, 3.0T and Intera 1.5T MR Systems are substantially equivalent to the primary predicate Achieva, Intera & Panorama 1.0T R2.5 (K063559, 01/04/2007), and the 1st legally marketed reference device Ingenia, Ingenia CX, Ingenia Elition, and Ingenia Ambition (K183063, 02/14/2019) and the 2nd legally marketed reference device Achieva R4 1.5T and Achieva R4 3.0T (aka Ingenia, K110151, 03/22/2011). The proposed Achieva 1.5T, 3.0T, and Intera 1.5T MR Systems are intended to be marketed with the following pulse sequences and coils that were 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. MultiBand SENSE (K143606) 8. 3D APT (K172920) 9. Achieva and Intera Coils

Here's a breakdown of the acceptance criteria and study information for the Philips Achieva 1.5T, 3.0T, and Intera 1.5T MR Systems, based on the provided text:

Important Note: The provided document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed clinical study for a novel device. As such, information typically found in a clinical study report (like detailed statistical methods, effect sizes for human readers with and without AI, or specific ground truth methodologies for a novel algorithm) is not present in this type of submission. The device described is a Magnetic Resonance (MR) system, which is a diagnostic imaging device, not a specific AI-powered diagnostic algorithm.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Size: Not applicable. This submission relies on non-clinical verification and validation tests rather than a separate clinical test set with patient data for assessing a new algorithm's performance. The device is an MR system, and the evaluation focuses on its technical performance and safety, not on the diagnostic accuracy of a new AI algorithm processing patient data.
• Data Provenance: Not applicable for a separate clinical test set. The non-clinical tests would involve engineering and performance evaluations in a controlled environment, not patient data from a specific country or collected retrospectively/prospectively for a new algorithm's evaluation.

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

• Number of Experts/Qualifications: Not applicable. As this involves non-clinical verification and validation testing of an MR system's performance and safety features (e.g., image quality, electromagnetic compatibility, software functionality), the "ground truth" would be established by engineering and quality assurance standards, benchmarks, and regulatory requirements, not by expert interpretation of patient images for diagnostic accuracy.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. Since there's no clinical test set requiring expert interpretation and consensus, there's no need for an adjudication method.

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

• MRMC Study Done: No. An MRMC study is typically performed to evaluate the impact of a new diagnostic algorithm on human reader performance (e.g., radiologists interpreting images with and without AI assistance). This submission pertains to an MR system, which is the imaging hardware and associated software to acquire images. The document explicitly states: "The proposed Achieva 1.5T, 3.0T, and Intera 1.5T MR Systems did not require a clinical study since substantial equivalence to the primary predicate device was proven with the verification/validation testing."
• Effect Size of Human Readers with vs. without AI: Not applicable, as no MRMC study was performed.

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

• Standalone Study Done: No. The device is an MR imaging system; it is not a standalone AI algorithm designed to provide diagnostic outputs independently. The output of the MR system (images, spectra, measurements) is intended to be interpreted by a trained physician.

7. Type of Ground Truth Used

• Type of Ground Truth: Not applicable in the context of diagnostic accuracy for a specific algorithm. For the non-clinical verification and validation, the ground truth would be defined by engineering specifications, regulatory standards, and established benchmarks for parameters like image quality, signal-to-noise ratio, spatial resolution, gradient linearity, safety limits (e.g., SAR), and software functionality.

8. Sample Size for the Training Set

• Training Set Sample Size: Not applicable. The document describes an MR imaging system, not a machine learning or AI algorithm that would typically require a training set of data. While the system's software components were developed and tested, the information provided does not indicate the use of a data-driven training set in the context of an FDA-cleared AI/ML algorithm.

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

• Ground Truth for Training Set: Not applicable, as there is no mention of a training set or an AI/ML algorithm requiring such.

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The ACHIEVA R4 1.5T and ACHIEVA R4 3.0T are magnetic resonance diagnostic devices that produce cross-sectional images, spectroscopy images and/or spectra in any orientation of the internal structure of the whole body. These images when interpreted by a trained physician, yield information that may assist in diagnosis. In addition, the ACHIEVA R4 1.5T and ACHIEVA R4 3.0T devices provide capabilities to perform interventional procedures in the head, body and extremities, which may be facilitated by MR techniques, such as real time imaging. Such procedures must be performed with MR compatible instrumentation as selected and evaluated by the clinical user.

The next generation Philips MR platform consists of either a 1.5T or 3.0T field generating superconducting unit. The radiofrequency receive chain consists of multiple coil types delivering a simplified and unique data acquisition system. The system is configured with a time-varying magnetic field system (gradients). Additional RF transmission is provided through an integrated RF body coil. The base software for the above mentioned system will be called Release 4. The magnetic resonance diagnostic device is used to produce cross-sectional images, spectroscopic imaging and/or spectra in any orientation of the internal structures of the whole body. These images when interpreted by a trained physician, yield information that may assist in a diagnosis. In addition, the device provides the capabilities to perform interventional procedures in the head, body and extremities, which may be facilitated by MR techniques, such as real time imaging. Such procedures must be performed with MR compatible instrumentation as selected and evaluated by the clinical user.

This submission is a 510(k) Pre-Market Notification for a Magnetic Resonance Diagnostic Device (MRDD). Unlike AI/ML device submissions, which often involve specific performance metrics like sensitivity and specificity, this type of submission focuses on demonstrating substantial equivalence to a predicate device. Therefore, the typical "acceptance criteria" and "device performance" in terms of clinical accuracy are not directly applicable in the same way. The "study" here refers to the comparisons made to establish substantial equivalence.

Here's the breakdown based on the provided text:

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

Since this is a 510(k) for an MRDD, the "acceptance criteria" and "reported device performance" are primarily focused on demonstrating that the new device (ACHIEVA R4 1.5T and ACHIEVA R4 3.0T) is equivalent to its predicate devices in terms of intended use, technological characteristics, and safety and effectiveness.

The subsequent points (2-9) are typical for AI/ML device studies involving clinical validation. For this specific 510(k) submission for an MRDD, these points are largely not applicable in the context of a dedicated clinical performance study as one would see for an AI algorithm. The determination of substantial equivalence for an MR Diagnostic Device typically relies on a comparison of technical specifications, intended use, and a demonstration that any technological differences do not raise new questions of safety or effectiveness. There is no mention of a separate "test set" or "ground truth" derived by experts for clinical performance evaluation of the device's diagnostic output as if it were an AI algorithm.

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

• Not Applicable. This submission does not describe a clinical performance study with a test set in the conventional sense used for AI/ML algorithms. Equivalence is based on comparison to predicate devices and adherence to performance standards.

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. No ground truth establishment by experts for a test set is described. The "ground truth" here is the established safety and effectiveness of the predicate devices.

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

• Not Applicable. No test set or corresponding adjudication method is discussed.

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 is not an AI-assisted device, and no MRMC study is mentioned.

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

• Not Applicable. This is a Magnetic Resonance Diagnostic Device (hardware and integrated software for image acquisition/reconstruction), not a standalone AI algorithm.

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

• Not Applicable. The "ground truth" for this 510(k) is the regulatory acceptance and established safety/effectiveness of the predicate MR systems themselves, demonstrated through their successful clearance and long-term use.

8. The sample size for the training set

• Not Applicable. This is not an AI/ML device, so there is no training set mentioned. The development process for an MRI system involves engineering design, testing against technical specifications, and adherence to regulatory standards rather than machine learning training.

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

• Not Applicable. As there is no training set for an AI/ML algorithm, there is no discussion of how ground truth was established for it.

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mDIXON is a software option intended for use on Intera 1.5T, Achieva 1.5T and Achieva 3T. MR Systems. It is indicated for magnetic resonance imaging of the chest, abdomen and pelvis. mDIXON is a multipoint (echo) method for 3D clinical imaging with the possibility to reformat into multiple planes (axial, sagittal and coronal). mDIXON provides improved fat suppression, increased scan speed in addition and/or an improved signal-to-noise relative to other current 3D volumetric fat suppressed imaging methods

The modified-DIXON (mDIXON) sequence is a novel two and multi-point method for 2D and 3D water-fat magnetic resonance imaging. mDIXON is a modification of previous DIXON implementations due to the unrestricted echo-time (TE) approach. This allows more freedom in protocol optimization resulting in more efficient (faster) scanning and an increase in signal to noise (SNR). Additionally, it provides a technique for improved fat suppression (in comparison to other current 3D volumetric fat suppressed imaging methods.) While the primary use is for torso imaging, it may also be applicable to other anatomies requiring in- and opposed-phase, water-only, and/or fatonly imaging. While the current 3D volumetric fat suppressed technique (e-THRIVE) is an imaging method, mDIXON is a multi echo sequence with multiple gradient echo readouts. Phase and amplitude of complex data acquired at different echo times are used to separate the water and fat signals. The separation is made possible by the chemical shift difference between water and fat. The resultant images can be reconstructed to produce "water-only" images, "fat-only" images and in-phase/opposed-phase images (synthesized from the acquired multiecho images). The fat suppression is enhanced especially at the edges of larger fields of view due to the mDIXON reconstruction algorithm and its use of the chemical shift difference between water and fat.

Acceptance Criteria and Study for Philips mDIXON Software Option (K102344)

The provided document describes the Philips mDIXON software option for MR systems. It highlights the device's improvements over existing 3D volumetric fat-suppressed imaging methods, specifically regarding fat suppression, scan speed, and signal-to-noise ratio (SNR). However, it does not explicitly state specific numerical acceptance criteria or detail a formal clinical study to prove these criteria.

Instead, the documentation focuses on:

• Verification and Validation (V&V) Testing: Stating that "mDIXON verification and validation tests were performed on the complete system relative to the requirement specification and risk management results. Corresponding test results are included in this submission." This implies that internal tests were conducted against pre-defined requirements, but the specifics of these requirements and their quantitative thresholds are not provided in this summary.
• Substantial Equivalence: The primary strategy for regulatory clearance (510(k)) relies on demonstrating substantial equivalence to predicate devices (INTERA 1.5T, ACHIEVA 1.5T, ACHIEVA 3.0T MR systems Release 2.5-series and the IDEAL software option). The core argument is that the mDIXON option does not introduce new risks and maintains the safety and effectiveness profile of these predicate devices, while offering improved performance.

Given the information, a table of explicit acceptance criteria and corresponding performance cannot be created directly as they are not explicitly mentioned in this summary. The assessment revolves around the general claims of improvement and the maintenance of safety and effectiveness as per predicate devices.

Based on the provided text, here's an analysis of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document does not explicitly list quantitative acceptance criteria for mDIXON. The performance claims are qualitative improvements over current 3D volumetric fat-suppressed imaging methods. The "acceptance" is implied by the successful completion of V&V testing and the FDA's determination of substantial equivalence.

2. Sample size used for the test set and the data provenance

• Sample Size: Not specified. The document only mentions "mDIXON verification and validation tests were performed," but no details on the number of cases, subjects, or data points in the test set.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Not specified. The document does not describe the involvement of human experts or ground truth establishment for specific test cases. The V&V process likely involved technical assessments rather than clinical evaluations by experts described here.

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

• Not specified. There is no mention of an adjudication process for a test set in the summary provided.

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, a MRMC comparative effectiveness study is not mentioned. This document pertains to a software option for an MR system, enhancing image acquisition and reconstruction, not a diagnostic AI tool for interpretation. Therefore, a study comparing human reader performance with and without AI assistance is not described or relevant for this type of device based on the provided information.

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

• The mDIXON is itself an "algorithm only" (software option) that generates images. The "standalone" performance
  is assessed by its ability to produce images with improved characteristics (fat suppression, scan speed, SNR) compared to existing methods. The validation of these characteristics is stated to have been performed through V&V tests, but no specific study details are given beyond this general statement. Essentially, the device is the algorithm, and its performance is evaluated on the quality of the output images.

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

• Not explicitly stated. Given the nature of the device (image acquisition/reconstruction), the "ground truth" for the V&V tests would likely be related to objective measures of image quality (e.g., quantitative fat suppression metrics, SNR measurements, acquisition time) compared against a reference standard or expected performance, rather than clinical ground truth like pathology or expert consensus on disease presence.

8. The sample size for the training set

• Not applicable/Not specified. The mDIXON sequence is a physics-based magnetic resonance imaging technique, not a machine learning model that requires a "training set" in the conventional sense. Its development would involve engineering and physics principles rather than data-driven training.

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

• Not applicable. See point 8.

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The ACHIEVA , INTERA and PANORAMA 1.0T Release 2.5-series are magnetic resonance diagnostic devices that produce cross-sectional images, spectroscopy images and/or spectra in any orientation of the internal structure of the whole body. These images when interpreted by a trained physician, yield information that may assist in diagnosis.

In addition the Achieva, Intera and Panorama 1.0T devices provide capabilities to perform interventional procedures in the head, body and extremities, which may be facilitated by MR techniques, such as real time imaging. Such procedures must be performed with MR compatible instrumentation as selected and evaluated by the clinical user.

The ACHIEVA , INTERA and PANORAMA 1.0T Release 2.5-series are the successor of the predicate devices ACHIEVA, INTERA and PANORAMA 1.0T release 2-series. The Release 2.5-series introduces the new functionalities:

• Cardiac MR Functional Analysis
• 4D-THRIVE
• Prospective Motion Correction
• Propeller
• CMR Prepulse Imaging.
• CMR IR-TFE T1 Imaging
• Cardiac MR Reporting
• Rapid Focus
• Rapid Locus
• Interventional procedures
• Achieva 3.0T mobile

This 510(k) summary does not contain information about specific acceptance criteria or a study proving the device meets those criteria. The document describes a new release (Release 2.5) of existing Magnetic Resonance Diagnostic Devices (MRDDs) – ACHIEVA, INTERA, and PANORAMA 1.0T. The submission focuses on describing new functionalities added to the previous versions and declares substantial equivalence to the predicate devices.

Therefore, many of the requested details about acceptance criteria, study design, and performance metrics are not present in the provided text.

Here's a breakdown of what can be extracted and what is missing:

1. A table of acceptance criteria and the reported device performance:
* Not provided. The document describes new features and functionalities but does not list specific quantitative acceptance criteria or performance metrics for these new features. The focus is on substantial equivalence to predicate devices, implying that the established safety and effectiveness of the previous versions are maintained.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective):
* Not provided. There is no mention of a specific test set, its sample size, or data provenance.

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 provided. No information regarding expert involvement in establishing ground truth for any test set is given.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
* Not provided. No adjudication method is mentioned as a test set itself is not detailed.

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, an MRMC study was not done, or at least not described. The device is a Magnetic Resonance Diagnostic Device, and the new features are primarily related to image acquisition, processing, and analysis tools (e.g., Cardiac MR Functional Analysis, 4D-THRIVE, Prospective Motion Correction, Rapid Focus). While these tools might assist human readers, the document does not present them as a standalone AI for diagnosis nor does it provide an MRMC study comparing human performance with and without these tools.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
* Not explicitly described as a standalone study in the traditional sense of an AI algorithm. The functionalities described (e.g., automatic segmentation, quantitative measurements) are components of the MR system and image analysis software, intended to be used by a "trained physician." There is no indication of a standalone performance evaluation of these components independent of human interpretation.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
* Not provided. No ground truth type is mentioned as no specific study with ground truth establishment is detailed.

8. The sample size for the training set:
* Not applicable / Not provided. The device is a medical imaging system with new software features. There is no mention of a 'training set' in the context of an AI/ML algorithm being trained, as understood by modern submissions. The development process would have involved engineering and validation, but not typically a "training set" for artificial intelligence or machine learning as such.

9. How the ground truth for the training set was established:
* Not applicable / Not provided. As no training set is mentioned, its ground truth establishment is also not described.

Summary of what is present:

The document primarily serves as a 510(k) summary for a new release of an existing device, focusing on substantial equivalence. It highlights new functionalities and capabilities added to the MR system but does not provide detailed performance metrics, clinical study results, or validation data for these new features in the way a submission for a novel diagnostic algorithm might. The implicit assumption is that the safety and effectiveness of the overall MR system remain equivalent to the predicate devices, and the new features enhance capabilities within that established framework.

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The ACHIEVA , INTERA and PANORAMA 1.0T Release 2-series are magnetic resonance diagnostic devices that produce cross-sectional images, spectroscopy images and/or spectra in any orientation of the internal structure of the whole body. These images when interpreted by a trained physician, yield information that may assist in diagnosis.

The ACHIEVA , INTERA and PANORAMA 1.0T Release 2-series are the successor of the predicate devices ACHIEVA, INTERA and PANORAMA 1.0T release 1-series. The Release 2-series introduces the new functionalities:

• Fiber Tracking .
  Diffusion Tensor Imaging (DTI) extends the functionality of Diffusion Weighted Imaging (DWI) to measure the directional dependence of the diffusion coefficient in tissue. With Fiber Tracking the directional dependency can be used to visualize the white matter structure in the brain.
• Smart Scan .
  SmartScan enables automatic planning of geometries and acquisition. When needed the user can control and confirm the automatically planned acquisition. The ExamCard provides the fully automated process of data acquisition.
  Regional Perfusion Imaging (Arterial Spin Labeling)
  Regional Perfusion Imaging with Arterial Spin Labeling provides a noninvasive acquisition method for selectively mapping of the flow territories and to determine the regional perfusion in the human brain.
• kt-BLAST and kt-SENSE. .
  Kt-BLAST (Broad-use Linear Acquisition Speed-up Technique) reduces scan time of dynamic and multi-phase studies by also using k-space data from other dynamics / phases. Kt-SENSE combines kt-BLAST with SENSE parallel imaging. Kt-blast and kt-SENSE can be applied to reduce scan time or improve temporal resolution of dynamic or multi-phase studies.
  The ACHIEVA, INTERA and PANORAMA 1.0T Release 2-series are the successors of the predicate devices ACHIEVA , INTERA and PANORAMA 1.0T Release 1-series. The design of the Release 2-series are based on the same software platform and hardware technology as their predicate devices.

The provided text describes a 510(k) submission for Philips Medical Systems' ACHIEVA, INTERA, and PANORAMA 1.0T Release 2-series of Magnetic Resonance Diagnostic Devices (MRDD). However, the document does not contain specific information regarding acceptance criteria, device performance studies, sample sizes, expert qualifications, or ground truth establishment.

Instead, the submission focuses on demonstrating substantial equivalence to their predicate devices (Release 1-series, K043147, K041602). The key argument for substantial equivalence is that the Release 2-series introduces new functionalities (Fiber Tracking, Smart Scan, Regional Perfusion Imaging, kt-BLAST, kt-SENSE) but does not induce any other risks than already indicated for their predicate devices with the same safety and effectiveness. This implies that the safety and effectiveness of the new features are considered to be within the established parameters of the predicate devices.

Therefore, I cannot populate the requested table or answer the specific questions about the study that proves the device meets acceptance criteria, as this information is not present in the provided document. The document essentially states that because the new device is a technological update that does not introduce new safety or effectiveness risks compared to its predecessor, specific new performance studies for acceptance criteria are not detailed in this submission.

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ACHIEVA 1.5T & INTERA 1.5T family consists of diagnostic devices that produce crosssectional images, spectroscopy images and/or spectra in any orientation of the internal structure of the whole body. These images when interpreted by a trained physician yield information that may assist in diagnosis.

The ACHIEVA 1.5T & INTERA 1.5T family is the successor of the predicate ACHIEVA family release 1-series. The design of ACHIEVA 1.5T & INTERA 1.5T family is based on the same software platform and hardware technology as its predicate device. All MR system parts of the ACHIEVA 1.5T & INTERA 1.5T family have the same appearance. The new gradient configurations are more powerful than their previous versions available on the Intera 1.5T and Achieva 1.5T systems. Especially the slewrate of these systems is improved. This improved slewrate allows for shorter echo times (TE), Echo Spacing (ES) and repetition times (TR).

The provided document is a 510(k) summary for the Philips ACHIEVA 1.5T & INTERA 1.5T family of Magnetic Resonance Diagnostic Devices (MRDD). This submission focuses on demonstrating substantial equivalence to a predicate device, rather than proving performance against specific acceptance criteria through a clinical study.

Therefore, many of the requested details about acceptance criteria, study design, sample sizes, expert involvement, and ground truth are not applicable (N/A) because they are typically part of a de novo submission or a premarket approval (PMA) application, not a 510(k) for substantial equivalence based on technology upgrades.

Here's a breakdown of the information that can be extracted or that is N/A based on the document:

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample size used for the test set and the data provenance:

• N/A. This submission leverages the substantial equivalence pathway, not a clinical trial with a distinct test set to evaluate performance against specific criteria. The comparison is primarily against the predicate device's existing clearance and safety profile.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• N/A. As no new clinical study with a test set for diagnostic accuracy was conducted for this 510(k), there wasn't a process described for establishing clinical ground truth for a test set. The "ground truth" implicitly relies on the established safety and effectiveness of the existing predicate device.

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

• N/A. No clinical test set requiring adjudication was used as part of this substantial equivalence submission.

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:

• N/A. This device (Magnetic Resonance Diagnostic Device) is a foundational imaging modality, not an AI-assisted diagnostic tool. No MRMC study or AI-related effectiveness study was performed or described.

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

• N/A. This device is an imaging hardware system, not an algorithm, and thus standalone algorithm performance is not applicable.

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

• N/A. For this 510(k) submission, the "ground truth" for demonstrating substantial equivalence is the performance and safety record of the predicate device (ACHIEVA family release 1-series, K043147). The submission asserts that the new device shares the same fundamental technology and does not introduce new safety or effectiveness concerns.

8. The sample size for the training set:

• N/A. This is a hardware and software upgrade to an existing MRI system. The concept of a "training set" as understood in machine learning/AI is not applicable here. The development would involve engineering design, testing, and validation against technical specifications rather than a data-driven training process in the AI sense.

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

• N/A. As explained above, the concept of a "training set" with established ground truth is not relevant to this type of device submission.

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ACHIEVA family consists of diagnostic devices that produce cross-sectional images, spectroscopy images and/or spectra in any orientation of the internal structure of the whole body. These images when interpreted by a trained physician, yield information that may assist in diagnosis.

The ACHIEVA family is the successor of the predicate Intera Achieva family release 1-series. The design of ACHIEVA family is based on the same software platform and hardware technology as its predicate device. All MR system parts of the ACHIEVA family have the same appearance. The ACHIEVA family is extended with enhancements and new functionalities for contrast enhanced MRA techniques and faster scanning techniques. Furthermore it is extended with SENSE Body wrap coil RF-coil and extension of multi-nuclei spectroscopy with 3T coils.

This document is a 510(k) summary for the Philips Medical Systems ACHIEVA family of Magnetic Resonance Diagnostic Devices (MRDD). It details the device's general information, predicate device, indications for use, and a statement of substantial equivalence.

Based on the provided text, here's a breakdown of the requested information:

1. Table of acceptance criteria and the reported device performance:

The document primarily focuses on substantial equivalence to a predicate device rather than presenting specific performance metrics against pre-defined acceptance criteria. The core assertion is that "The ACHIEVA family does not induce any other risks than already indicated for its predicate device with the same safety and effectiveness." This implies that the acceptance criteria are met by virtue of being equivalent to the already cleared predicate device.

Therefore, a table of specific numerical acceptance criteria and reported device performance from this document cannot be directly constructed as those details are not provided. The "performance" reported is essentially that it is substantially equivalent to the predicate.

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

The document does not provide any details on a specific test set, sample size, or data provenance as it relies on substantial equivalence. There is no mention of a particular study with a test set.

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):

This information is not available in the document as no specific test set or study requiring ground truth establishment is described.

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

This information is not available in the document as no specific test set or study requiring adjudication 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:

This document describes a Magnetic Resonance Diagnostic Device (MRDD), which is an imaging system, not an AI-assisted diagnostic tool. Therefore, an MRMC comparative effectiveness study regarding human reader improvement with AI assistance is not applicable and not mentioned.

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

This document describes an imaging device, not an algorithm, so a standalone algorithm-only performance study is not applicable and not mentioned.

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

This information is not available in the document as no specific study involving ground truth establishment is described. The basis for clearance is substantial equivalence to a predicate device.

8. The sample size for the training set:

This information is not available in the document as no machine learning algorithm development with a training set is described.

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

This information is not available in the document as no machine learning algorithm development with a training set is described.

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