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The device is used for radiological guidance and visualization during diagnostic, interventional and surgical procedures on all patients. The device is to be used in health care facilities both inside and outside the operating room, sterile as well as non-sterile environment in a variety of procedures. Applications: Orthopedic, Neuro, Abdominal, Vascular, Thoracic, Cardiac

The proposed Zenition 90 is a mobile, diagnostic X-ray imaging and viewing system. It is designed for medical use in healthcare facilities where X-ray imaging is needed. The system comprises of two main components: the C-arm stand and a Mobile View Station (MVS).

This document is a 510(k) summary for the Philips Zenition 90, which is an Image-Intensified Fluoroscopic X-Ray System. The summary asserts substantial equivalence to a predicate device (Zenition 70, K212813) rather than proving performance against specific acceptance criteria for a novel device or AI algorithm.

Therefore, many of the requested details, such as those related to acceptance criteria for device performance, sample sizes for test and training sets for an AI algorithm, expert ground truth establishment, adjudication methods, and MRMC studies, are not applicable in the context of this 510(k) summary. The document does not describe a study proving the device meets acceptance criteria in the way one would for a software or AI/ML device. Instead, it demonstrates compliance with recognized standards and substantial equivalence to a predicate device.

However, I can extract the relevant information provided:

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

The document does not provide a table of acceptance criteria and reported device performance in the typical sense for a new AI/software function. Instead, it compares the proposed device (Zenition 90) features and specifications to those of the predicate device (Zenition 70) to establish substantial equivalence. The "acceptance criteria" here appear to be adherence to the predicate device's performance characteristics or recognized industry standards.

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

This document discusses non-clinical performance testing and non-clinical validation testing for the device's intended use, claims, safety measures, instructions, and usability. It does not specify sample sizes of imaging data for a test set, nor the provenance of such data (e.g., country of origin, retrospective/prospective). This is because it is not a submission for a new AI/ML algorithm that requires clinical image data validation for its performance metrics. Instead, it relies on demonstrating compliance with recognized standards for the x-ray system as a whole.

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. As this is not an AI/ML algorithm submission requiring ground truth for image interpretation, there is no mention of experts establishing ground truth for a test set.

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

Not applicable. There is no test set in the context of image interpretation that would require an adjudication method.

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 interpretation device that would involve an MRMC study.

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

Not applicable. This is not a software algorithm, but a complete x-ray system.

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

Not applicable. No ground truth for image interpretation is described or required for this type of submission.

8. The sample size for the training set

Not applicable. There is no mention of a training set as this is not an AI/ML algorithm.

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

Not applicable.

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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.

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ClarifEye is intended to be an intra-operative image-guidance tool used during surgical and interventional therapy. It provides assistance to the performing physician to align a device with a virtual path that is planned on a 3D volume of the anatomy. This alignment is provided in the following ways:

-The virtual path is superimposed with a live video image of the area of interest.

-The position of the ClarifEye Needle is superimposed with the video images of the area of interest and/or the 3D images of the anatomy.

ClarifEye is intended to be used on patients who have been elected for procedures where a straight, rigid device is placed in the spine, such as sacral, lumbar and thoracic pedicle screw placement. ClarifEye is indicated for procedures where a reference to bony anatomical structures can be established using skin markers as a reference.

The ClarifEye Needle is a manual, surgical instrument intended to be used during spine surgery to facilitate placement of guidewires. The needle may be used as part of a planning and intraoperative guidance system (i.e. Philips intra-operative image guidance tool) to enable open or percutaneous image guided therapy. The ClarifEye Needle is indicated for use during posterior pedicle screw procedures, such as in the sacral, lumbar and thoracic spinal regions, in which the use of image guided surgery may be appropriate.

The ClarifEye Needle is EtO sterilized, for single use only and have to be disposed after use, according to local waste disposal methods for potentially bio hazardous material.

ClarifEye is a software medical device that is intended to be an intra-operative image-guidance tool used during surgical and interventional therapy.

It will be offered as an optional accessory to the Philips interventional fluoroscopic X-ray system, from which it receives 2D X-ray and video images. ClarifEye implements an automatic reconstruction (algorithm) to create 3D CBCT images from a rotational scan acquired on the X-ray system.

Clarif Eye integrates the live video images of the surgical view and live 2D X-rav image which it overlays on the planned path shown in the reconstructed 3D CBCT image to provide navigational assistance, in real-time. ClarifEye provides assistance to the performing physician to align a device, such as a needle with a virtual path that is planned on a 3D image of the anatomy. The created 3D planning can be overlaid on live video images ("Augmented View") or live 2D fluoroscopy images, to monitor device deployment during the procedure.

ClarifEye is intended to be used in combination with the compatible ClarifEye Needle and ClarifEye Markers.

The ClarifEye Needle is for optional use only, when needle tip tracking is desired.

Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

Study Details:

The document describes several non-clinical studies (phantom, pig cadaver, human cadaver) and one clinical study to demonstrate the device's performance and meet acceptance criteria.

1. Non-Clinical Performance Data (K9, K10):

• Test Set Sample Size:
  • Phantom Tests: Not specifically enumerated, but refers to "phantom tests" (multiple tests indicated by plural).
  • Pig Cadaver Study: Not explicitly stated, but measured on "thorocolumbar vertebrae."
  • Human Cadaver Study: Not explicitly stated, but measured on "thoracolumbar."
• Data Provenance:
  • Phantom Tests: In-house (implied by context of non-clinical testing for compliance with ASTM F2554-10).
  • Pig Cadaver Study: In-house (implied by "a pig cadaver study demonstrated...").
  • Human Cadaver Study: In-house (implied by "a human cadaver study demonstrated...").
• Number of Experts & Qualifications for Ground Truth: Not specified for these non-clinical studies. The ground truth would likely be established by precise measurements and engineering methods.
• Adjudication Method: Not specified.
• MRMC Comparative Effectiveness Study: No, this section describes standalone performance of the device in controlled environments.
• Standalone Performance: Yes, these are standalone (algorithm only or device-only) performance evaluations.
• Type of Ground Truth:
  • Phantom Tests: Positional and angular accuracy measurements against known targets.
  • Cadaver Studies: Accuracy of screw/needle placement relative to planned paths, measured with imaging or physical means.
• Training Set Sample Size: Not applicable. These are performance evaluations of the device, not descriptions of algorithm training.
• Ground Truth for Training Set: Not applicable.

2. Usability Validation (K9, K10):

• Test Set Sample Size:
  • ClarifEye: "both orthopedic/neuro spine surgeons and monitoring nurse/technicians." Not a specific number, but mentions representative user groups.
  • ClarifEye Needle: "representative users." Not a specific number.
• Data Provenance: Simulated use environment, implying in-house or controlled testing facility.
• Number of Experts & Qualifications for Ground Truth: Orthopedic/neuro spine surgeons and monitoring nurse/technicians as test users, evaluating usability and effectiveness. The "ground truth" here is the user's experience and assessment against predetermined usability criteria.
• Adjudication Method: Not specified, but likely based on user feedback and recorded observations against objective usability criteria.
• MRMC Comparative Effectiveness Study: No.
• Standalone Performance: Yes, this is a standalone usability evaluation of the device.
• Type of Ground Truth: User feedback and expert assessment within a simulated clinical workflow.
• Training Set Sample Size: Not applicable.
• Ground Truth for Training Set: Not applicable.

3. In-house Simulated Use Design Validation (K9):

• Test Set Sample Size: "Clinical Scientists/Marketing specialists that fulfill the intended user profile." Not a specific number.
• Data Provenance: In-house simulated use environment.
• Number of Experts & Qualifications for Ground Truth: Clinical Scientists/Marketing specialists, acting as surrogate users to validate workflow and user needs.
• Adjudication Method: Not specified, but likely based on successful execution of predefined validation protocols.
• MRMC Comparative Effectiveness Study: No.
• Standalone Performance: Yes.
• Type of Ground Truth: Successful execution of predefined device workflows in a phantom model.
• Training Set Sample Size: Not applicable.
• Ground Truth for Training Set: Not applicable.

4. Clinical Study (Summary of Clinical Performance Data) (K10, K11):

• Test Set Sample Size: Twenty (20) subjects.
• Data Provenance: Prospective, single-arm, single-center observational study with patients outside the United States.
• Number of Experts & Qualifications for Ground Truth: An unspecified number of experts (implied to be clinical personnel, likely radiologists or surgeons) evaluated the screw placement based on post-procedural CBCT. The grading criteria were the "recognized Gertzbein classification" (and its adaptation for cervical screws).
• Adjudication Method: "Grading of pedicle screw placement was done according to the recognized Gertzbein classification for the lumbar and thoracic region and slightly adapted for the cervical screw placements." The details of how agreement was reached among multiple graders, if any, are not provided.
• MRMC Comparative Effectiveness Study: No. This was an observational study evaluating the accuracy of the device in clinical use, not comparing human readers with and without AI assistance.
• Standalone Performance: While used by surgeons, the study primarily assesses the accuracy of the "navigation software" (ClarifEye) in guiding pedicle screw placement, making it a standalone assessment of the device's accuracy capabilities in a clinical context. The outcome measure (accuracy of screw placement) directly measures the system's performance.
• Type of Ground Truth: Post-procedural CBCT images, evaluated against the Gertzbein classification by clinical experts. This is considered expert consensus/imaging-based ground truth (radiographic outcome).
• Training Set Sample Size: Not applicable. This is a clinical validation study, not algorithm training.
• Ground Truth for Training Set: Not applicable.

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The Philips Hemodynamic Application is intended for use by professional healthcare providers for physiologic/ hemodynamic monitoring, medical data processing and analytical assessment.

The software may be used to display analyze surface Electrocardiogram (ECG), Respiration. Invasive Blood Pressure (IBP), Pulse Oximetry (SpO2), End Tidal CO2 (ETCO2), Fractional Flow Reserve (FFR), Instant Wave-Free Ratio (iFR), Non-Invasive Blood Pressure (NIBP), surface body Temperature and thermal Cardiac Output.

The software is intended for use with other devices, such as physiological monitoring systems, information management systems, image acquisition and other medical devices.

Use of the software in combination with physiological monitoring system is not intended to be used where unattended patient monitoring is desired, or in situations where arrhythmia detection is required.

The software in combination with an information management system provides the ability to transmit patient data files for storage, viewing and analysis at distributed locations via the intranet or internet.

The software is indicated for use in the following areas; (interventional) cardiology, electrophysiology,

The Philips Hemodynamic Application is indicated for use for all human patients of all ages.

Philips Hemodynamic Application is a new software medical device that enables invasive investigation of cardiac and vascular disease. It will be offered as an optional accessory to the Xper Flex Cardio Physiomonitoring System, (K101571). Currently, the functionality offered by the Philips Hemodynamic Application is provided by "Hemodynamic Control Software" of the currently marketed and predicate Xper Flex Cardio Physiomonitoring System.

The software connects to the patient monitor (i.e. the Xper Flex Cardio Physiomonitoring System) and during the intervention continuously acquires realtime physiological data and alarms. In addition, Philips Hemodynamic Application provides the following functionality:

• Visualize and analyze: surface ECG, Respiration rate (RR), Invasive . Blood Pressure (IBP), Pulse Oximetry (SpO2), End Tidal CO2 (etCO2), Noninvasive monitoring and recording of Non-Invasive Blood Pressure (NIBP), Body surface temperature (Tskin);
• Provide Hemodynamic calculations: Fractional Flow Reserve (FFR), Instant Wave-Free Ratio (iFR), thermal cardiac output parameters, valve area and valve gradient.

Furthermore, Philips Hemodynamic Application also interfaces with Xper Information Management (XperIM) System (K101571) which it can transfer data to for the purpose of data collection/display, processing and patient reporting.

The provided text does not contain detailed acceptance criteria or a specific study proving the device meets those criteria with numerical performance metrics. Instead, it describes non-clinical performance and validation testing that supports the device's substantial equivalence to a predicate device.

Here's an analysis based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics. It states: "All these tests were used to support substantial equivalence of the subject device and demonstrate that Philips Hemodynamic Application: • complies with the aforementioned international and FDA-recognized consensus standards and FDA guidance documents, and • meets the acceptance criteria and is adequate for its intended use."

It also mentions "Algorithm verification was performed using calibrated simulator tools that confirmed the algorithm was correctly implemented in the product. Results demonstrated that all executed verification tests were passed." and "In-house simulated use design validation was performed with experienced Clinical Marketing specialists that fulfill the intended user profile... As part of the validation, the implemented algorithms were evaluated as part of the workflow. Results demonstrated that all executed validation protocols were passed."

This indicates that acceptance criteria were met, but the specific numerical targets and measured performance are not detailed in this summary. The acceptance criteria seem to be binary (pass/fail) based on compliance with standards and successful algorithm implementation and workflow validation.

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

• Test Set Sample Size: Not explicitly stated. The verification and validation activities are described qualitatively.
• Data Provenance: Not specified. The verification was done using "calibrated simulator tools," and validation involved "simulated use environment" and "in-house simulated use design validation." This suggests internal testing without specific patient data provenance mentioned.

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

• Number of Experts: Not explicitly stated.
• Qualifications of Experts: For usability validation, it involved "cardiologists and monitoring nurse/technicians." For in-house simulated use design validation, it involved "experienced Clinical Marketing specialists that fulfill the intended user profile." Specific years of experience or board certifications are not provided.

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

Not mentioned in the document.

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 MRMC comparative effectiveness study involving human readers or AI assistance is mentioned. The device, "Philips Hemodynamic Application R1.0," is described as a software medical device for physiological/hemodynamic monitoring, data processing, and analytical assessment, not specifically an AI-based interpretation tool that assists human readers in diagnostic tasks in the way typically associated with MRMC studies.

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

Algorithm verification was performed using "calibrated simulator tools that confirmed the algorithm was correctly implemented in the product." This could be considered a form of standalone performance assessment for the algorithms' mathematical correctness. However, it's not a standalone clinical performance study. The device is intended for use by "professional healthcare providers," implying human-in-the-loop operation.

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

The document implies that the ground truth for algorithm verification was derived from the expected outputs of the "calibrated simulator tools." For validation, the "implemented algorithms were evaluated as part of the workflow" by experienced specialists, suggesting a functional ground truth based on expected performance in a simulated clinical workflow. No mention of pathology or outcomes data is made.

8. The sample size for the training set:

The document does not describe the use of a training set for machine learning. The device is presented as applying "comparable technology as implemented in the Hemodynamic Control Software module" and implementing algorithms for hemodynamic calculations, including an iFR algorithm, without indicating machine learning or AI models requiring discrete training data.

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

Not applicable, as no training set for machine learning is mentioned.

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SmartPerfusion assists in the diagnosis of perfusion of tissues based on digital subtraction angiography (DSA) and can be used for any location in the body where DSA is used.

SmartPerfusion is a software product (Interventional Tool) that provides color coded representation of a digital subtraction angiography (DSA). It can visualize multiple functional parameters related to the time density function. It also provides a comparison between pre-, peri-, and post- procedural color coded images. SmartPerfusion is provided as an accessory to the Philips Interventional X-ray system.

The provided text does not contain detailed acceptance criteria or a specific study that demonstrates the device meets these criteria in the format requested. The document is a 510(k) summary for the Philips SmartPerfusion device. It states that non-clinical performance testing demonstrated compliance with FDA recognized consensus standards and guidance documents, and that verification and validation tests were performed. However, it does not provide:

• A table of specific acceptance criteria and detailed reported device performance.
• Information on sample sizes used for a test set, data provenance, or the number/qualifications of experts for ground truth establishment.
• Adjudication methods.
• Results of a multi-reader multi-case (MRMC) comparative effectiveness study, nor did it mention an effect size for human readers with and without AI assistance.
• A standalone algorithm performance study.
• The type of ground truth used (e.g., pathology, outcomes data).
• The sample size for the training set or how ground truth for the training set was established.

The document indicates that the device does not require a clinical study because substantial equivalence with the predicate device (2D Perfusion, K132147) was demonstrated through:

• Indications for use
• Technological characteristics
• Non-clinical performance testing, including safety and effectiveness.

It explicitly states: "SmartPerfusion does not require a clinical study as the substantial equivalence Summary of with the predicate device 2D Perfusion (K132147) is demonstrated with the following attributes: Indication for use; ● Technological characteristics; . . Non-clinical performance testing, including safety and effectiveness. Non-clinical performance data provides sufficient evidence that the subject device works as intended The verification and validation test results of the modified device SmartPerfusion described above support the safety and effectiveness of the product. It conforms to the intended use, the user needs and the claims and is therefore considered substantially equivalent to the predicate device 2D Perfusion (K132147)."

Therefore, based on the provided text, I cannot complete the requested tables and information fields for acceptance criteria and study details.

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Philips IntelliSpace Portal Platform is a software medical device that allows multiple users clinical applications from compatible computers on a network.

The system allows networking, selection, processing and filming of multimodality DICOM images.

This software is for use with off-the-shelf PC computer technology that meets defined minimum specifications .

Philips IntelliSpace Portal Platform is intended to be used by trained professionals, including but not limited to physicians and medical technicians.

This medical device is not to be used for mammography.

The device is not intended for diagnosis of lossy compressed images.

Philips IntelliSpace Portal Platform is a software medical device that allows multiple users to remotely access clinical applications from compatible computers on a network. The system allows networking, selection, processing and filming of multimodality DICOM images. This software is for use with offthe-shelf PC computer technology that meets defined minimum specifications.

The IntelliSpace Portal Platform communicates with imaging systems of different modalities using the DICOM-3 standard.

Here's an analysis of the provided text regarding the acceptance criteria and study for the IntelliSpace Portal Platform (K162025):

The submitted document is a 510(k) Premarket Notification for the Philips IntelliSpace Portal Platform. This submission aims to demonstrate substantial equivalence to a legally marketed predicate device (GE AW Server K081985).

Important Note: The document focuses on demonstrating substantial equivalence for a Picture Archiving and Communications System (PACS) and related functionalities. Unlike AI/ML-driven diagnostic devices, the information provided here does not detail performance metrics like sensitivity, specificity, or AUC against a specific clinical condition using a test set of images with established ground truth from a clinical study. Instead, the acceptance criteria and "study" refer to engineering and functional verification and validation testing to ensure the software performs as intended and safely, consistent with a PACS system.

Here's the breakdown based on your requested information:

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

   The document does not provide a table with specific quantitative acceptance criteria or reported performance results in the classical sense (e.g., sensitivity, specificity, accuracy percentages) because it's for a PACS platform, not a diagnostic AI algorithm for a specific clinical task.

   Instead, the "acceptance criteria" for a PACS platform primarily relate to its functional performance, compliance with standards, and safety. The reported "performance" is a successful demonstration of these aspects.

   Acceptance Criteria (Inferred from regulatory requirements and description)	Reported Device Performance (as stated in the submission)
   Compliance with ISO 14971 (Risk Management)	Demonstrated compliance with ISO 14971. (p. 9)
   Compliance with IEC 62304 (Medical Device Software Lifecycle Processes)	Demonstrated compliance with IEC 623304. (p. 9)
   Compliance with NEMA-PS 3.1-PS 3.20 (DICOM Standard)	Demonstrated compliance with NEMA-PS 3.1-PS 3.20 (DICOM). (p. 9)
   Compliance with FDA Guidance for Content of Premarket Submissions for Software Contained in Medical Devices	Demonstrated compliance with relevant FDA guidance document. (p. 9)
   Meeting defined functionality requirements and performance claims (e.g., networking, selection, processing, filming of multimodality DICOM images, multi-user access, various viewing/manipulation tools as listed in comparison tables)	Verification and Validation tests performed to address intended use, technological characteristics, requirement specifications, and risk management results. Tests demonstrated the system meets all defined functionality requirements and performance claims. (p. 9)
   Safety and Effectiveness equivalent to predicate device	Demonstrated substantial equivalence in terms of safety and effectiveness, confirming no new safety or effectiveness concerns. (p. 9, 10)

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

   This type of information is not provided in this document. Since the submission is for a PACS platform and not a diagnostic AI algorithm, there is no mention of a "test set" of clinical cases or patient data in the context of diagnostic performance evaluation. The "testing" refers to software verification and validation, which would involve testing functionalities rather than analyzing a dataset of medical images.

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 applicable/not provided. As explained above, there is no "test set" of clinical cases with ground truth established by medical experts for diagnostic performance.

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

   This information is not applicable/not provided. There is no clinical "test set" requiring adjudication for diagnostic performance.

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 multi-reader multi-case (MRMC) comparative effectiveness study was not performed. This device is a PACS platform, not an AI-assisted diagnostic tool designed to improve human reader performance for a specific clinical task. The submission explicitly states: "The subject of this premarket submission, ISPP does not require clinical studies to support equivalence." (p. 9).

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

   No, a standalone performance study (in the context of an AI algorithm performing a diagnostic task) was not done. This device is a software platform for image management and processing, intended for use by trained professionals (humans-in-the-loop) for visualization and administrative functions.

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

   This information is not applicable/not provided. There is no ground truth data in the context of diagnostic accuracy for this PACS platform submission. The "ground truth" for its functionality would be defined by its requirement specifications, and testing would verify if those specifications are met.

8. The sample size for the training set

   This information is not applicable/not provided. This device is a PACS platform, not an AI/ML algorithm that requires a "training set" of data in the machine learning sense. The software development process involves design and implementation, followed by verification and validation, but not training on a dataset of images to learn a specific task.

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

   This information is not applicable/not provided. As there is no "training set," there is no ground truth establishment for it.

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This system is a Magnetic Resonance Medical Electrical Systems indicated for use as a diagnostic device.

The system can produce cross-sectional images, spectroscopic images and/or spectra in any orientation of the internal structure of the head, body or extremities.

Magnetic Resonance images represent 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. The image acquisition process can be synchronized 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 the 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. For some studies the use of contrast agents can be essential. Their application is subject to local medico-legal regulations and to their appropriateness to assist the diagnosis and therapy planning as judged by a trained physician.

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 1.5T and Ingenia 1.5T S R5.2 with ScanWise Implant feature is provided with a 70 cm magnet. ScanWise Implant functionality enables MR technologists to implement an improved and controlled workflow for MR Conditional implants. The feature consists of an extension to the Patient Registration User Interface where the information relevant to MR Conditional device labeling can be assessed, controlled and reviewed. The ScanWise Implant feature allows the user at the examination level to define restrictions on the 'active fields' generated by the MR system.

The proposed Ingenia 1.5T and Ingenia 1.5T S R5.2 with ScanWise Implant feature also consolidates separately-cleared novel functionalities, and minor hardware and software changes since the clearance of the currently marketed and predicate device, Ingenia R4 (K110151, 03/22/2011).

Following minor hardware and software changes are covered in this submission:

1. (Hardware) Enhanced Patient Communication User Interface Module, IEC/ISO compliant symbols.
2. (Hardware) New computing platform and peripherals for MR Spectrometer (DDAS).
3. (Software) User Interface layout modifications for scan preparation, sequence planning (geometries and parameters), and data processing and viewing.
4. (Software) Planning on cine images.
5. (Software) SAR related parameters (SED). Pregnancy status related to Normal Mode.
6. (Software) Parameter optimization for the reconstruction algorithms.
7. (Software) Partial NSA algorithm in reconstruction.
8. (Software) AutoVoice, using pre-recorded spoken instructions.
9. (Software) VCG, optimized electrode placement and enhanced algorithm.
10. (Software) ComforTone: mechanical resonance frequency dependent timing adjustments of sequences for lower acoustic noise.
11. Enhanced sequences:
    a. AutoSpair.
    b. TSE flow compensation enhancement.
    c. Optimized 3D TSE flip angle sweeps per anatomy.
    d. ENCASE: 3D encoding.
    e. CardiacQuant: triggered T1 mapping sequence.
    f. pCASL.
    g. DTI enhancements.

The provided text primarily focuses on demonstrating substantial equivalence of the Philips Ingenia 1.5T and Ingenia 1.5T S R5.2 with ScanWise Implant feature to a predicate device, rather than providing a detailed study proving the device meets specific performance acceptance criteria.

However, based on the Summary of Non-Clinical Performance Data and Summary of Clinical Data sections, we can infer some information relevant to acceptance criteria and the "study" conducted.

Here's an attempt to structure the information as requested, drawing conclusions from the provided text where explicit details are not present.

Acceptance Criteria and Device Performance

The document doesn't provide a table of explicit, quantitative acceptance criteria for the device's performance (e.g., sensitivity, specificity, accuracy for a specific diagnostic task). Instead, the acceptance criteria are implicitly tied to demonstrating safety and effectiveness compared to a predicate device, and compliance with various standards.

Study Information:

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

   • The document states that "Non-Clinical verification and or validation tests have been performed." and mentions "sample clinical images." However, no specific sample size for a test set (e.g., number of patients, number of images) is provided.
   • Data Provenance: Not specified. Given the lack of a clinical study, the "sample clinical images" were likely used for qualitative assessment during non-clinical validation.

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

   • Not applicable. No clinical study involving expert interpretation for ground truth establishment is described. The device's primary pathway to market is substantial equivalence to a predicate, relying on non-clinical testing and engineering validation.

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

   • Not applicable. No clinical study with expert adjudication is described.

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 done. The document explicitly states: "The proposed Ingenia 1.5T and Ingenia 1.5T S R5.2 with ScanWise Implant feature did not require clinical study since substantial equivalence to the primary currently marketed and predicate device was demonstrated with the following attributes: Design features; Indication for use; Fundamental scientific technology; Non-clinical performance testing; and Safety and effectiveness."
   • The device being cleared is an MR system, not an AI-assisted diagnostic tool that would typically undergo such a study to evaluate reader improvement. The "ScanWise Implant" feature is described as enabling an "improved and controlled workflow" for MR Conditional implants, acting as a control mechanism rather than an interpretive AI.

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

   • Not applicable in the typical sense of an AI algorithm. This is a hardware and software update to an MRI system. The performance evaluated was the system's ability to operate safely and effectively, and to manage parameters for MR Conditional implants, rather than an "algorithm only" diagnostic performance. The text mentions "Parameter optimization for the reconstruction algorithms" and "Partial NSA algorithm in reconstruction" which are standalone algorithms within the system, but their performance isn't quantified in terms of diagnostic metrics.

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

   • For the non-clinical validation, the "ground truth" was implicitly defined by engineering specifications, safety standards, and the performance characteristics of the predicate device. The text doesn't describe pathology-confirmed diagnoses or outcomes data as "ground truth."

7. The sample size for the training set:

   • Not applicable in the context of machine learning training data. This submission describes a new version of an MRI system, not an AI/ML device that requires a distinct training set. The various software enhancements and algorithms mentioned (e.g., parameter optimization for reconstruction, DTI enhancements, ComforTone) were developed and refined through engineering processes, not via a labelled "training set" like a typical AI application.

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

   • Not applicable. (See point 7).

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mDIXON XD is a software option intended for use on Achieva, Ingenia 1.5T & 3T MR Systems. It is indicated for use in Magnetic Resonance Imaging of the human body. mDIXON XD is an enhanced 2-point fat-free mDIXON technology, available for gradient echo and spin echo acquisitions. mDIXON XD enables the combination of a fat-free technology with the MultiVane XD motion-free technique for anatomies such as head. mDIXON XD also enables subtractionless MR Angiography in areas of the body where exogenous contrast media has been approved for MR imaging.

mDIXON XD provides an improved water/fat reconstruction algorithm (7-peak fat modeling, B0correction and fat-shift correction). mDIXON XD relies on the 2-point mDIXON fat-free technology making use of unrestricted echo times and is capable of generating four image contrasts (Water, Fat, In-Phase, Out-Phase) in one scan. mDIXON XD can be used with the following:

• Multivane XD (motion correction) for the head
• MR Angiography (subtractionless MRA), removes the need for the subtraction of the pre-● and post-contrast enhanced images.
• Gradient echo (FFE Fast Field Echo) and turbo spin echo (TSE) sequences to achieve ● efficient fat-free scanning compatibility, even at large FOV or sub-millimetric resolutions
  mDIXON XD is supported on the following systems:
• 3.0T Ingenia
• 3.0T Achieva ●
• 1.5T Ingenia
• 1.5T Achieva ●
  The functionality is supported on all available gradient performance levels. Optimized protocols will be provided for the different performance points. mDIXON XD is supported on the centralized data acquisition systems of the Achieva systems as well as the digitally networked data acquisition system of the Ingenia systems. The data acquisition system is fully transparent to the pulse sequences and mDIXON XD reconstructions.

The provided text is a 510(k) summary for the Philips Medical Systems' mDIXON XD device. It describes the device, its intended use, and provides a brief summary of nonclinical and clinical tests. However, it does not contain specific acceptance criteria, detailed study designs, or quantitative performance metrics as requested in the prompt.

Therefore, I cannot provide a table of acceptance criteria and the reported device performance, nor can I answer questions about sample sizes for test/training sets, data provenance, number/qualifications of experts, or adjudication methods, as this information is not present in the provided document.

Here's what can be extracted based on the document:

1. Table of Acceptance Criteria and Reported Device Performance:

• Acceptance Criteria: Not explicitly stated as quantitative metrics. The document broadly states that the device was "successful" and "passed" clinical user needs.
• Reported Device Performance:
  • For total body and head/neck examinations: Image Quality, fat suppression, and fat suppression over large FOV were similar or better with mDIXON XD compared to e-Thrive.
  • For cardiac examinations: Image quality and fat suppression were better with mDIXON XD.
  • For MultiVane XD in combination with mDIXON TSE: Motion related artifacts were reduced compared to mDIXON TSE alone.
  • MultiVane XD with mDIXON XD can be used with T2 in head imaging.

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

• Sample Size for Test Set: Not specified.
• 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:

• This information is not provided. The term "clinical user needs" suggests clinical evaluation, but details on expert involvement are absent.

4. Adjudication method for the test set:

• Not specified.

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 MRMC comparative effectiveness study involving human readers and AI assistance is described. The comparison is between mDIXON XD and existing technologies (e-Thrive, mDIXON TSE alone).

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

• The testing described is on the "device" as a whole, which is a software option for MR systems. It's an "algorithm only" performance in the sense that it evaluates the output of the software. However, it's not a standalone AI device in the modern sense of a diagnostic AI assisting or replacing a human. The clinical validation assesses image quality and artifact reduction.

7. The type of ground truth used:

• The "ground truth" implicitly used is expert judgment and assessment of "Image Quality," "fat suppression," and "motion related artifacts" as perceived by "clinical user needs." It's based on subjective clinical assessment rather than objective pathology or outcomes data.

8. The sample size for the training set:

• Not specified. The document describes verification and validation but does not detail a separate training set, suggesting this is a software update/feature validation rather than a deep learning algorithm that requires a distinct training phase.

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

• Neither a training set nor its ground truth establishment is mentioned.

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