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The Allura Xper R9 series (within the limits of the used Operating Room table) are intended for use to perform: · Image guidance in diagnostic, interventional and minimally invasive surgery procedures for the following clinical application areas: vascular, non-vascular, cardiovascular and neuro procedures. · Cardiac imaging applications including diagnostics, interventional and minimally invasive surgery procedures. Additionally: · The Allura Xper R9 series can be used in a hybrid Operating Room. · The Allura Xper R9 series contain a number of features to support a flexible and patient centric procedural workflow.

The Allura Xper R9 is classified as an interventional fluoroscopic X-ray system. The primary performance characteristics of the Allura Xper R9 interventional fluoroscopic X-ray system include: - Real-time image visualization of patient anatomy during procedures - Imaging techniques and tools to assist interventional procedures - Post processing functions after interventional procedures ● - Storage of reference/control images for patient records ● - Compatibility to images of other modalities via DICOM ● - . Built in radiation safety controls This array of functions offers the physician the imaging information needed to perform minimally invasive interventional procedures. The Allura Xper R9 is available in a comparable set of configurations for monoplane models as the currently marketed and predicate devices Allura Xper FD series / Allura Xper OR Table series R8.2.1. Configurations are composed of detector type, (monoplane) geometry, table type and available image processing. The monoplane (single C-arm) systems are categorized into X-ray systems and the configurations are differentiated by the following features: - . 12 inch Flat detector (FD12) - 15 inch Flat detector (FD15) ● - 20 inch Flat detector (FD20) . Additionally, identical to the predicate devices, all configurations of the Allura Xper R9 are compatible for use in a hybrid operating room when supplied with a compatible operating room table, and can be optionally equipped with the ClarityIQ image processing algorithms.

The provided document is a 510(k) summary for the Philips Medical Systems Nederland BV Allura Xper R9, an image-intensified fluoroscopic x-ray system. The document focuses on demonstrating substantial equivalence to a predicate device (Allura Xper FD series / Allura Xper OR Table series R8.2.1) rather than presenting a study to prove a device meets specific acceptance criteria based on its own reported performance.

Therefore, many of the requested elements (acceptance criteria table, sample size for test set, data provenance, number of experts, adjudication method, MRMC study, standalone performance, training set size, training set ground truth) are not detailed or applicable in the context of this 510(k) submission, which is primarily a comparative assessment.

However, I can extract the relevant information that is present:

Acceptance Criteria and Reported Device Performance

The core "acceptance criteria" in this context is substantial equivalence to the predicate device, specifically in terms of:

1. Indications for Use: The modified device (Allura Xper R9) must have the same indications for use as the predicate.
2. Technological Characteristics: Despite modifications, the fundamental scientific technology must be the same, and changes must not raise new questions of safety or effectiveness.
3. Safety and Effectiveness: Performance testing (non-clinical) must demonstrate that the device is as safe and effective as the predicate device.

The "reported device performance" is a demonstration of this substantial equivalence.

Other requested information:

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

   • Not specified for non-clinical performance tests. The document refers to "software verification testing," "non-clinical validation testing," and "imaging performance metrics" but does not detail sample sizes, data provenance (e.g., country), or whether it was retrospective/prospective outside of referring to "sample clinical images" which were not required for substantial equivalence.

2. 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 specified for the non-clinical performance tests that established substantial equivalence. The document mentions "Signed statements from a board certified radiologist demonstrate that sample clinical images are of adequate quality for use in diagnostic and interventional procedures." This was additional evidence, not the primary means of establishing substantial equivalence for the device's technical aspects.

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

   • Not specified. The non-clinical tests relate to engineering and technical performance (software verification, compliance with standards, imaging performance metrics). Expert adjudication in the traditional sense for diagnostic accuracy is not mentioned as a primary method for demonstrating substantial equivalence.

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 MRMC study was done or reported. This device is an X-ray imaging system, not an AI-powered diagnostic tool. The submission is a 510(k) for an updated fluoroscopic X-ray system, demonstrating equivalence to an existing one.

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

   • Not applicable in the context of a general fluoroscopic X-ray system. The "standalone" performance here refers to the system's ability to capture and process images according to its specifications and regulatory standards. The non-clinical performance testing (software verification, imaging performance) served this purpose.

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

   • For the non-clinical performance tests, "ground truth" was established by compliance with recognized international standards (IEC, ISO), FDA guidance documents, and engineering specifications. For example, "Results demonstrated that all executed tests were passed" for software verification. "Equivalent imaging performance" was measured against the predicate device based on metrics outlined in specific FDA guidance.

7. The sample size for the training set

   • Not applicable. This submission is for an imaging device, not an AI/machine learning algorithm requiring a "training set."

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

   • Not applicable, as there is no training set for this type of device.

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Philips IntelliSpace Cardiovascular software product is an integrated multimodality image and information system designed to perform the necessary functions required for import, export, storage, archival, review, analysis, quantification, reporting and database management of digital cardiovascular images, waveforms and data related to cardiology.

Philips IntelliSpace Cardiovascular offers support for third party applications in order to enable the use of commercially available tools and specified applications for analysis, quantification and reporting. It allows multiple users fast access to, and exchange of specific and/or multiple cardiology exams.

Philips IntelliSpace Cardiovascular software runs on standard information technology hardware, utilizing the standard information technology operating systems and user interface. Communication and data exchange are done using standard protocols. Philips IntelliSpace Cardiovascular will also be made available for use on specified Cardiovascular Monitoring Systems, which use suitable hardware components.

The modular design allows configurability to tailor the image import, archive and communications solution to one's particular budgetary and performance needs. The number of modalities and reporting and/or viewing sites can be configured per system.

IntelliSpace Cardiovascular is a comprehensive cardiac image and information management solution designed to provide clinicians with convenient access to the detailed records of all cardiac across their complete cardiovascular care continuum. The application also provides hospital administrators and department managers with detailed operational information, as well as productivity and outcomes reporting. Key components include a wide range of detailed clinical modules that capture data during diagnostic/therapeutic procedures and patient follow-up encounters. Interfaces to other systems and devices within the cardiology departments as well as across the enterprise system, such as HIS/EMR, are available.

The solution supports a remote deployment model.

The provided text is a 510(k) summary for the Philips IntelliSpace Cardiovascular device. It explicitly states that no clinical studies were required or performed to support the equivalence of this device. Therefore, it does not contain the specific information requested about acceptance criteria or a study proving the device meets those criteria.

However, the document does contain information about how the device's performance was evaluated, which I can summarize with the disclaimer that it is not a clinical study in the traditional sense outlined in your request.

Here's a breakdown of the available information:

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

The document does not provide a table of precise acceptance criteria with corresponding performance metrics. Instead, it describes general verification and validation activities.

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

The document does not detail specific sample sizes for test sets, data provenance, or whether testing was retrospective or prospective. It refers to general "system level tests, performance tests, and safety testing."

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 provided because no clinical studies involving ground truth assessment by experts are mentioned.

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

This information is not 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 MRMC comparative effectiveness study was done. The device is an image and information management system, not an AI-assisted diagnostic tool in the sense that would typically require such a study for its clearance as described here.

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

The device is a software product designed for image and information management, with human users ("clinicians," "users of the equipment"). A standalone algorithm-only performance study as typically understood for AI diagnostics is not applicable and was not performed. The performance evaluation focused on the system's functionality and its equivalence to a predicate device.

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

Since no clinical studies or diagnostic performance evaluations were conducted to establish "ground truth" for diagnostic accuracy, this information is not applicable and not provided. The testing focused on technical and functional verification rather than diagnostic accuracy against a ground truth.

8. The sample size for the training set

This information is not applicable and not provided. The device is not described as an AI/ML system that would require a "training set" in the context of diagnostic algorithm development.

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

This information is not applicable and not provided, as there is no mention of a training set for an AI/ML algorithm.

In summary, the Philips IntelliSpace Cardiovascular device (K153022) was cleared through the 510(k) pathway by demonstrating substantial equivalence to a predicate device (Philips Xcelera K061995) based on non-clinical testing. This non-clinical testing included risk analysis, product specifications, design reviews, and verification and validation activities to ensure the device met its defined functionality and performance requirements and complied with relevant standards. No clinical studies or human performance evaluations were deemed necessary or conducted for its clearance.

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The Philips Spectral CT Applications support viewing and analysis of images at energies selected from the available spectrum in order to provide information about the chemical composition of the body materials and/or contrast agents. The Spectral CT Applications provide for the quantification and graphical display of attenuation, material density, and effective atomic number. This information may be used by a trained healthcare professional as a diagnostic tool for the visualization and analysis of anatomical and pathological structures.

The Spectral enhanced Advanced Vessel Analysis (SAVA) application is intended to assist clinicians in viewing and evaluating CT images, for the inspection of contrast-enhanced vessels.

The Spectral enhanced Comprehensive Cardiac Analysis (SCCA) application is intended to assist clinicians in viewing and evaluating cardiovascular CT images.

The Spectral enhanced Tumor Tracking (sTT) application is intended to assist clinicians in viewing and evaluating CT images, for the inspection of tumors.

The Spectral CT Applications package introduces a set of three SW clinical applications: spectral enhanced Comprehensive Cardiac Analysis (sCCA), spectral enhanced Advanced Vessel Analysis (sAVA), and spectral enhanced Tumor Tracking (sTT). Each application provides tools that assist a trained personal in visualization and analysis of anatomical and pathological structures.

The sCCA application is targeted to assist the user in analysis and diagnostic of Cardiac Cases, as contrast enhanced and ECG triggered scans. The application input is a cardiac case that was acquired on the IQon CT scanner; the application takes the user through typical workflow steps that allow him to extract qualitative and quantitative information on the coronary tree and chambers. The output of this application is information on physical (length, width, volume) and composition properties (Effective Atomic number, Attenuation, HU) of the coronary vessel & findings along it.

The sAVA application is targeted to assist the user in analysis and diagnostic of CT Angiography cases, as contrast enhanced and whole body CT-angiography scans. The application input is a CT Angiography case that was acquired on the IQon CT scanner; the application takes the user through typical workflow steps that allow him to extract qualitative and quantitative information on the vessel of interest. The output of this application is information on physical (length, width, volume) and composition properties (Effective Atomic number, Attenuation, HU) of the vessels & findings along it.

The sTT application is targeted to assist the user in analysis of tumors, as contrast enhanced, soft tissue oriented, and whole body scans. The application input is a tumor suspected contrast enhanced case that was acquired on the IQon CT scanner; the application takes the user through typical workflow steps that allow him to extract qualitative and quantitative information on the tumor of interest. The output of this application is information on physical (length, width, volume) and composition properties (Effective Atomic number, Attenuation, HU) of the tumor.

The provided text describes the Philips Spectral CT Applications, a set of software clinical applications (sCCA, sAVA, sTT) designed to assist clinicians in viewing and evaluating CT images with spectral data. While the document mentions verification and validation, it does not provide explicit acceptance criteria in a quantitative table or detailed performance metrics against those criteria. It generally states that "SW requirements were met" and "intended uses and defined user needs were met."

Therefore, I cannot populate a table of acceptance criteria and reported device performance with specific numerical values from the provided text. However, I can extract information related to the study that proves the device meets its intended uses and user needs, as described in the "Summary of Clinical Testing" section.

Here's a breakdown of the available information:

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

No explicit quantitative acceptance criteria or corresponding reported device performance metrics are provided in the document. The document states that "SW requirements were met" and "intended uses and defined user needs were met."

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

• Test Set Sample Size: "clinical datasets" – specific number not provided.
• Data Provenance: The datasets were "derived from Philips IQon Spectral CT system (K133674)." The country of origin is not specified, but the manufacturer is Philips Medical Systems Nederland B.V. and the regulatory contact is in the USA. The data appears to be retrospective, as it's referred to as "clinical datasets" used for validation, not newly acquired data specifically for this study.

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

• Number of Experts: "Philips Internal certified radiologists" – specific number not provided.
• Qualifications of Experts: "certified radiologists" - specific experience level (e.g., 10 years) not provided. They are referred to as representing "a typical user."

4. Adjudication method for the test set:

• Adjudication Method: Not explicitly stated. The document mentions that "The evaluators were questioned against each of the intended uses and provided score to describe their level of satisfaction." This suggests individual evaluation rather than a formal consensus or adjudication process among multiple readers for ground truth establishment.

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: No MRMC comparative effectiveness study, comparing human readers with and without AI assistance, is described. The validation focused on whether the applications meet intended uses and user needs, as evaluated by radiologists. The applications are described as "assistive tools" for clinicians.

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

• Standalone Performance: Not explicitly evaluated or described. The validation confirms that the applications "allow visualization, manipulation and analysis of spectral data" and "assist clinicians in viewing and evaluating CT images." This implies human interaction as part of the intended use.

7. The type of ground truth used:

• Ground Truth Type: The ground truth for the validation appears to be based on the subjective evaluation and "satisfaction scores" provided by "Philips Internal certified radiologists" against the stated intended uses and user needs. It is based on expert consensus/evaluation of the utility of the application's outputs, rather than a separate, independent "ground truth" like pathology or outcomes data.

8. The sample size for the training set:

• Training Set Sample Size: Not specified. The document primarily discusses verification and validation using "datasets that were generated by the Philips IQon Spectral CT system" and "clinical datasets." It does not provide details on a distinct training set.

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

• Ground Truth for Training Set: Not specified, as details about a separate training set or how its ground truth was established are not provided in the document.

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MultiBand SENSE is a software option intended for use on Achieva and Ingenia 1.5T & 3.OT MR Systems. It's indicated for use in magnetic resonance imaging of the brain for BOLD fMRI. MultiBand SENSE consists of an acquisition and reconstruction technique allowing simultaneous excitation of multiple volumes to accelerate imaging acquisition times or increasing coverage or resolution without increasing scan time.

The MultiBand SENSE technique enables simultaneous excitation and acquisition of multiple volumes or slices for the purpose of speeding up acquisition times or increasing coverage or resolution at constant scan time. The simultaneous excitation is done using a multi-band radiofrequency pulse. The unfolding of the simultaneously acquired volumes is done using the SENSE algorithm. The unfolding process (solving the linear equation of the SENSE algorithm) is improved by introducing a linear phase over k-space in the volume direction resulting in a spatial shift of the aliased pixels. The phase shift is applied by additional blip-gradients in the slice direction or switching between different RF pulses, and compensated for in reconstruction by a translation of the coil sensitivity data before the SENSE unfolding. The feature consists of:

• Modulated RF pulses exciting 2 or more slices
• Blip-gradients to introduce a phase shift for improved unfolding ●
• New parameterization of the SENSE calculations ●
• Shifting coil sensitivities in reconstruction to correct for linear phase shift. ●

MultiBand SENSE 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. MultiBand SENSE 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 MultiBand SENSE pulse sequences and reconstructions.

The main functional units in the software are:

• Methods (acquisition of MR signals by means of MR pulse sequences) -
• Reconstruction (transforming the MR signals to images) -
• -Patient Administration (storing of the images in the database and providing access)
• Viewing (display of images) -

The technical impact of the feature MultiBand SENSE comprises:

• -Methods: Introduction of a modulated RF pulse. Apply blipped gradients. Provide new parameterization for SENSE reconstruction.
• Reconstruction: Read new parameterization of SENSE calculations. Shift coil sensitivities before SENSE calculations.

No off-the-shelf software is used for the feature MultiBand SENSE. The off-the-shelf software used in the basic MR system is cleared. MultiBand SENSE is not designed to be connected to an external network.

MultiBand SENSE does not require any change of the hardware platform. The extension introduced by Multiband SENSE, are in methods pulse sequence code, and in reconstruction only for a new parameterization of a cleared SENSE unfolding calculation. Those run on the host computer characteristics :

• Manufacturer: HP; Model: Z420; Processor clock: 3.5 GHz; RAM: 64 GB RAM; -Processors: six core with hyper threading
• Operating system: Windows 7, 64 bits -

The only new element for the operator of the Multiband SENSE feature in this clinical routine workflow is:

• Protocol selection: The operator selects an ExamCard with Multiband SENSE protocols -
• Planscan phase: Optionally the operator may want to change the predefined Multiband acceleration factor.

All other steps are not changed. The generated image types can be viewed, post-processed, printed and archived as any other image type.

This document is a 510(k) Summary for the Philips MultiBand SENSE device, a software option for MRI systems. It focuses on demonstrating substantial equivalence to a predicate device. The information provided is primarily related to verification and validation, rather than a detailed comparative effectiveness study with specific acceptance criteria and statistical analysis as might be found in a clinical trial report.

Here's an analysis of the provided text in relation to your questions, noting where information is explicitly stated, implied, or absent:

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

The document does not present a formal table of quantitative acceptance criteria and corresponding device performance metrics in the way a statistically powered study might. Instead, it describes general successful outcomes of verification and validation testing.

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The Veradius Unity device is intended to be used and operated by: adequately trained, qualified, and authorized health care professionals such as physicians, surgeons, cardiologists, and radiographers, who have full understanding of the safety information and emergency procedures as well as the capabilities and functions of the device. The device is used for radiological guidance and visualization during diagnostic, interventional, and surgical procedures on all patients except neonates (birth to one month), within the limits of the device is to be used in health care facilities both inside and outside the operating room, in sterile environments, in a variety of procedures.

The proposed Veradius Unity is a counterbalanced C-arm with a thin flat detector x-ray system. The system consists of two main component parts: the C-arm stand (comprising X-ray generator and X-ray tube, Flat Detector and the X-ray control user interface) and the mobile viewing station (comprising the image processor, monitors, user interface for image/patient handling and optionally an integrated workstation)

Here's the breakdown of acceptance criteria and the study that proves the device meets them, based on the provided text:

Device Name: Veradius Unity

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not specify a sample size for a test set in the context of imaging data or patient studies. The "testing" referred to is primarily non-clinical performance and verification/validation testing of the device's components and new features. The provenance of any underlying data used for these tests is also not mentioned (e.g., country of origin, retrospective/prospective). It explicitly states: "The subject of this premarket submission Veradius Unity did not require clinical studies to support substantial equivalence because the Veradius Unity utilizes identical components throughout the imaging chain and the system's equivalent performance in light of the modifications could be supported through verification and validation testing alone."

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

Not applicable. As no clinical studies were performed, there was no separate "ground truth" for a test set established by medical experts in the typical sense of evaluating diagnostic accuracy. The testing focused on device functionality and engineering performance.

4. Adjudication method for the test set

Not applicable, as no clinical studies with a test set requiring adjudication were conducted.

5. If a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done

No, an MRMC comparative effectiveness study was not done. The submission explicitly states that "clinical studies to support substantial equivalence" were not required.

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

Not applicable, as this device is an imaging system (hardware and software), not an AI algorithm designed to operate entirely without human input for image interpretation or diagnosis. The "Outline tool" and "ClearGuide" are features embedded within the system to assist human users, not standalone diagnostic algorithms.

7. The type of ground truth used

For the non-clinical performance tests, the "ground truth" was established by engineering specifications, international standards (IEC, ISO), and FDA guidance documents. The device's performance was validated against these predefined technical requirements and functional standards. There was no "pathology" or "outcomes data" ground truth collection mentioned.

8. The sample size for the training set

Not applicable. This document describes a medical imaging device (C-arm X-ray system), not an AI algorithm that would typically require a "training set" of data for machine learning. The "improvements" are hardware and software features, not AI model updates.

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

Not applicable, as there was no training set for an AI algorithm.

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