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The NeuViz Prime Multi-Slice CT Scanner System can be used as a whole body computed tomography X-ray system featuring a continuously rotating X-ray tube and detector array. The acquired X-RAY transmission data is reconstructed by computer into cross-sectional images of the body from either the same axial plane taken at different angles or spiral planes taken at different angles.

The NeuViz Prime Multi-Slice CT Scanner System is composed of a gantry, a patient couch, an operator console and includes image acquisition hardware and software, and associated accessories. It is designed to be a head and whole body X-ray computed tomography scanner which features a continuously rotating tube-detector system and functions according to the fan beam principle. The system provides the filter back-projection (FBP) and iterative reconstruction algorithm(ClearView cleared in K133373) to reconstruct images. The end user can choose to apply either ClearView or the FBP to the acquired raw data. The system software is an interactive program used for X-ray scan control, image reconstruction, and image archive/evaluation. It provides the following digital image processing and visualization tools:

• · Support following scan speed: 0.259s(option), 0.32s(option), 0.374s(option), 0.4s(option), 0.5s, 0.6s, 0.8s, 1.0s, 1.5s, 2.0s.
• . Surview scan
• Dual surview
• Spiral scan
• Axial scan
• Image reconstruction
• Plan scan
• Patient information management
• Patient information registration
• Protocol selection
• O-Dose
• · Bolus tracking
• SAS
• Home
• Film
• Report
• 2D
• MPR
• 3D
• VE(Virtual Endoscopy)
• Vessel Analysis
• Dicom Viewer
• Bar code Reader
• Dual Monitor
• CCT Scan
• ClearView
• iHD
• Cardiac Scan
• Dual Energy Scan and Reconstruction ●
• Dental Analysis
• Virtual Colonoscopy
• Brain Perfusion
• Body Perfusion
• · Lung Nodule Analysis
• Lung Density Analysis
• · Coronary Analysis
• Cardiac Calcium Scoring .
• Cardiac Function Analysis
• Cardiac Viewer
• Fat Analysis
• CTDSA
• Tumor Assessment
• · Preprocessing function
• AVW.Cloud
• · Prism Viewer

The provided document is a 510(k) summary for the NeuViz Prime Multi-Slice CT Scanner System. It describes the device's characteristics and its substantial equivalence to a predicate device but does not contain acceptance criteria for specific performance metrics or detailed results of a study designed to prove the device meets those criteria, especially in an AI context. The document focuses on showing non-inferiority to an existing device rather than meeting specific quantifiable performance targets with clinical evidence in the format you requested for an AI/ML device.

However, I can extract information related to the device's performance characteristics, safety, and the "clinical testing" that was performed, even if it doesn't align perfectly with the AI/ML-focused questions.

Here's an attempt to answer your questions based on the provided text, while acknowledging the limitations for AI-specific criteria:

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

The document does not provide a table of explicit acceptance criteria with numerical targets for clinical performance (e.g., sensitivity, specificity, accuracy for a specific disease detection task). Instead, it states that "the subject device performs as intended" and "NeuViz Prime can be used as defined in its clinical workflow and intended use," and that "The Results indicated that the images were of diagnostic quality."

For image quality metrics, it lists:

• CT number accuracy and uniformity
• MTF (Modulation Transfer Function)
• Noise
• Slice sensitivity profiles
• CTDI (Computed Tomography Dose Index)

The document doesn't report specific numerical acceptance criteria for these image quality metrics, nor does it provide the measured performance values for them. It only states that "The result of all conducted testing was found acceptable to support the claim of substantial equivalence."

It does provide CTDI Dose values for the subject device compared to the predicate:

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

The "clinical testing" involved an "image evaluation" where "sample images were provided to show the performance of the system in presence of implants." This suggests a test set composed of image data.

• Sample Size: Not explicitly stated. The document refers to "images of the brain, chest, abdomen and spine/extremities of the body area," implying multiple images, but no specific count is given.
• Data Provenance: Not explicitly stated. It is likely retrospective data as it describes an "image evaluation" of existing images rather than a prospective clinical trial. The location of data origin (e.g., country) is not mentioned.

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

• Number of Experts: One.
• Qualifications of Experts: "A qualified radiologist." No specific experience level (e.g., "10 years of experience") is provided.

4. Adjudication method for the test set

• Adjudication Method: Not explicitly an adjudication method in the sense of multiple readers reaching consensus. The images were "scored using a 5 point Likert scale by a qualified radiologist." This implies a single-reader assessment rather than a consensus or adjudicated ground truth process.

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, a multi-reader multi-case comparative effectiveness study was not reported. The provided document describes a CT scanner system, not an AI-assisted diagnostic tool for which human reader improvement would be typically measured. The "clinical testing" described was an image quality assessment by a single radiologist.

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

• Standalone Performance: Not applicable in the context of this submission. The NeuViz Prime is a CT scanner, a hardware device that generates images. While it has reconstruction algorithms (FBP, ClearView, iHD) and image processing tools (e.g., Lung Nodule Analysis, Cardiac Calcium Scoring), the submission focuses on the overall performance of the imaging system and its substantial equivalence to a predicate device, not on the standalone performance of an AI algorithm intended for diagnostic interpretation. It does mention "The main algorithm of Prism Viewer Application is identifying of substances and calculating of dual energy images," but no standalone performance metrics are provided for this.

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

• Type of Ground Truth: The document refers to an "image evaluation" where images were "scored using a 5 point Likert scale by a qualified radiologist." This points to expert opinion/scoring as the basis for evaluating "diagnostic quality." It does not mention pathology, outcomes data, or a consensus of multiple experts.

8. The sample size for the training set

• Training Set Sample Size: Not applicable/not provided. This document describes the clearance of a CT scanner system, not an AI/ML algorithm that would typically have a distinct training set. While the system's reconstruction algorithms (like ClearView, iHD) would have been developed and "trained" or optimized during their creation, this document does not provide details on their specific training sets.

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

• Ground Truth for Training Set: Not applicable/not provided. Similar to point 8, the document does not discuss the training of AI/ML models. If "ClearView" or other advanced algorithms involved machine learning at their core, the method for establishing their training ground truth is not detailed in this 510(k) summary.

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The system is intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission projection data from the same axial plane taken at different ans the capability to image whole organs in a single rotation. Whole organs include but are not limited to brain, heart, liver, kidney, pancreas, etc. The system may acquire data using Axial, Cine, Helical, Cardiac, and Gated CT scan techniques from patients of all ages. These images may be obtained either with or without contrast. This device may include signal analysis and display equipment supports, components and accessories.

This device may include data and image processing to produce images in a variety of trans-axial and reformatted planes. Further, the images can be post processed to produce additional imaging planes or analysis results

The system is indicated for head, whole body, cardiac, and vascular X-ray Computed Tomography applications.

The device output is a valuable medical tool for the diagnosis of disease, trauma, or abnormality and for planning, guiding, and monitoring therapy.

If the spectral imaging option is included on the system can acquire CT images using different kV levels of the same anatomical region of a patient in a single rotation from a single source. The differences in the energy dependence of the attenuation coefficient of the different materials provide information of body materials. This approach enables images to be generated at energies selected from the visualize and analyze information about anatomical and pathological structures.

GSI provides information of the chemical composition of renal calculation and graphical display of the spectrum of effective atomic number. GSI Kidney stone characterization orovides addin the characterization of uric acid versus nonuric acid stones. It is intended to be used as an adjunct to current standard methods for evaluating stone etiology and composition.

The Revolution CT is a multi-slice (256 detector row) CT scanner consisting of a gantry, patient table, scanner desktop (operator console), system cabinet, power distribution unit (PDU), and interconnecting cables. The system includes image acquisition hardware, image acquisition and reconstruction software, and associated accessories.

GE has modified the cleared Revolution CT (K133705) within our design controls to include the Gemstone™ Spectral Imaging (GSI) Option. GSI is the state-of-the-art single source, projection-based, spectral CT application. It is GE's unique dual energy design and implementation which offers clear advantage over traditional dual source Dual Energy implementation. This feature has been previously cleared on Discovery CT750 HD (K081105, K120833) and it is fundamentally the same technology on Revolution CT. Revolution CT however offers a few technology improvements to enable Volume GSI with up to 80mm GSI zcollimation, 245mm/s GSI volumetric scan speed, dose neutrality and more improved workflow to support GSI in routine scanning.

The provided text is a 510(k) summary for the GE Revolution CT with GSI option. The document describes the device, its intended use, and indicates that it is substantially equivalent to predicate devices. However, it does not explicitly detail acceptance criteria in a quantitative table or a specific study proving the device meets acceptance criteria in the way often associated with performance claims for AI/ML devices.

Instead, the document focuses on demonstrating substantial equivalence by outlining:

• Technological similarities and differences with predicate devices.
• Compliance with various industry standards (IEC, 21CFR Subchapter J, NEMA XR-25, XR-26, XR-28, XR-29).
• Adherence to quality system regulations (21CFR 820 and ISO 13485).
• Results from non-clinical (phantom) testing and clinical testing.

The clinical testing aimed to evaluate "image quality related to diagnostic use, reduction of metal artifacts using the MAR algorithm, and suppression of iodine in contrast enhanced acquisitions using VUE algorithm." The evaluation was based on a 5-point Likert scale by radiologists, indicating a subjective assessment of image quality and clinical acceptance rather than predefined quantitative performance metrics or acceptance criteria for a specific diagnostic task.

Therefore, many of the requested items cannot be fully extracted as they are not explicitly or quantitatively provided in the document.

Here's an attempt to answer based on the available information:

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

The document does not provide a quantitative table of acceptance criteria for diagnostic performance metrics (e.g., sensitivity, specificity, AUC) and therefore no numerical performance results against such criteria. The clinical assessment focused on "acceptable diagnostic imaging performance" and "image quality," which are qualitative statements derived from expert review.

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

• Sample Size (Test Set): 51 subjects.
• Data Provenance: The clinical data was collected from two sites: one in the US and one in Canada. The study was prospective in the sense that it involved recruitment of patients and collection of clinical images for the specific evaluation.

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: 6 board-certified and qualified radiologists.
• Qualifications: "board certified and qualified radiologists at different institutions in the United States of America." (Specific years of experience are not mentioned).
• Ground Truth establishment for Test Set: This refers to the radiologists evaluating the images for "clinical acceptance and image quality using a 5 point Likert scale." This implies a subjective expert assessment of image quality for diagnostic use, reduction of metal artifacts, and suppression of iodine, rather than a definitive "ground truth" for a specific disease outcome or pathology.

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

• "Each data set was read by three different radiologists depending on their area of expertise." This implies a consensus or individual review approach, but the specific adjudication method (e.g., how disagreements between the three radiologists were resolved or combined into a single outcome) is not specified. It's unclear if a formal adjudication process like 2+1 or 3+1 was used, or if individual reads were separately analyzed.

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

The document describes an evaluation of the device’s image quality and diagnostic performance by multiple radiologists ("multi-reader"). However, it is not an MRMC comparative effectiveness study comparing human readers with AI assistance vs. without AI assistance. The study evaluated the images produced by the device (which includes the GSI option, a form of advanced image processing, but not explicitly framed as an 'AI assistance' to human interpretation in the common sense of AI CAD/X systems) directly for their diagnostic quality. Therefore, there's no reported effect size of human improvement with vs. without AI assistance.

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

The GSI functionality itself could be considered a form of "algorithm only" processing that produces specific images/data (e.g., material density maps, monochromatic images, virtual unenhanced images, information for kidney stone characterization). The document states GSI "provides information of the chemical composition of renal calculi by calculation and graphical display of the spectrum of effective atomic number" and "provides additional information to aid in the characterization of uric acid versus non-uric acid stones." This output is interpreted by humans. The testing described focuses on the quality of these generated images/information as assessed by radiologists, not on an automated diagnostic output from the algorithm itself without human interpretation. So, while GSI involves algorithms, it's not presented as a standalone diagnostic algorithm in the typical sense of AI/CAD systems providing a diagnosis.

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

• The "ground truth" for the clinical evaluation of the test set was essentially expert assessment/consensus based on image quality and clinical acceptance using a Likert scale. It was not based on definitive pathology, histology, or long-term outcomes data for establishing true disease presence or absence for a diagnostic accuracy study. For kidney stone characterization, it 'provides additional information' and is 'intended to be used as an adjunct to current standard methods for evaluating stone etiology and composition,' implying that the ultimate ground truth for stone composition would come from other established methods.

8. The sample size for the training set

The document does not explicitly mention a "training set" with a specified sample size. This device is an imaging system (CT scanner) with advanced image processing (GSI), not a machine learning model that would typically have a distinct training set for diagnostic classification in the same way. The technologies are based on physics and signal processing, using proprietary algorithms.

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

Since a "training set" for a machine learning model is not explicitly described, neither is the method for establishing its ground truth. The development of the GSI algorithms would have involved engineering and possibly empirical data to refine the material decomposition and image generation, but this is not characterized as a "training set" in the context of supervised learning.

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The ASIR-V Reconstruction Option is intended to produce cross-sectional images of the head and body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial, Helical (Volumeric), and Cardiac acquisitions for all ages.

When used, it allows for an alternate reconstruction method designed to reduce image noise and streak artifact, increase resolution and improve low contrast detectability in images produced using raw Computed Tomography data from the GE CT scanner. The ASIR-V Reconstruction Option can be used to reduce noise in images and also to reduce the dose required for diagnostic CT imaging, including scans of the head, chest, heart, abdomen and pelvis. The ASIR-V Reconstruction Option may also improve the image quality of low dose non-diagnostic Filtered Back Projection images such that they become diagnostic.

ASiR-V reconstruction option is for use with the GE CT Scanners.

The GE ASIR-V Reconstruction Option is an alternate CT image reconstruction option for GE CT Systems to Filtered Back Projection, the advanced GE iterative reconstruction software Veo Reconstruction Option (K103489) and the ASiR reconstruction option.

This reconstruction technique is the advanced GE iterative reconstruction method intended to be used when higher image quality and/or lower dose acquisitions are desired for all routine cases to improve image performance such as Low Contrast Detectability, Image Noise, Spatial Resolution, artifact reduction, etc. Image quality improvements and dose reduction depend on the clinical task, patient size, anatomical location, and clinical practice.

The GE ASIR-V Reconstruction Option when used with the CT System performs as well as or better than Filtered Back Projection and uses operating principles derived from ASIR and Veo.

Here's a summary of the ASiR-V device's acceptance criteria and the study that proves it meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the ASiR-V Reconstruction Option, relative to Filtered Back Projection (FBP), are outlined below along with the reported performance from phantom-based tests.

2. Sample Size and Data Provenance (Test Set)

• Sample Size for Clinical Image Evaluation: 96 patient exams
• Data Provenance: The document does not explicitly state the country of origin for the patient data, nor does it explicitly state if it was retrospective or prospective. It refers to "sample clinical images" and "retrospective clinical read," suggesting the data was pre-existing and evaluated retrospectively. The study was conducted by GE Healthcare, which has manufacturing locations in Japan and the USA, and China, but this doesn't specify data origin.

3. Number and Qualifications of Experts for Test Set Ground Truth

• Number of Experts: Four radiologists.
• Qualifications: The document does not specify the years of experience or specific subspecialties of the radiologists.

4. Adjudication Method (Test Set)

The radiologists rated the diagnostic image quality (IQ) using a 5-point Likert scale for 192 images (96 exams, each reconstructed with FBP and ASiR-V). After this individual rating, the "total score of each recon algorithm is compared." This suggests individual assessments were aggregated, but no formal adjudication method like a 2+1 or 3+1 consensus process between the radiologists is explicitly described. They simply "rated" the images and then "total scores" were compared.

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

A multi-reader multi-case (MRMC) comparative effectiveness study was conducted for diagnostic image quality.

• Effect Size of Human Readers with AI vs. without AI assistance: The document states that ASiR-V "demonstrated the clinical diagnostic quality of the ASiR-V images across various CT platforms and patient anatomies" and that "the total score of each recon algorithm is compared." It concludes that ASiR-V provides "equivalent or better performance (no loss of diagnostic quality...)" compared to FBP. However, it does not provide a specific quantitative effect size for how much human readers improved with ASiR-V assistance compared to reading FBP images alone. The evaluation focused on the diagnostic quality of the ASiR-V images themselves, not on the delta improvement in reader performance.

6. Standalone (Algorithm Only) Performance

Yes, standalone (algorithm only) performance was done. The majority of the acceptance criteria (dose reduction, LCD improvement, noise reduction, spatial resolution improvement, artifact reduction) were demonstrated through phantom-based tests, which assesses the algorithm's direct impact on image characteristics without human interaction in the loop for initial assessment. The model observer study for LCD also represents a standalone assessment.

7. Type of Ground Truth Used

• For performance metrics (dose, LCD, noise, spatial resolution, artifact reduction): Phantom-based measurements and model observer analysis were used, which are objective quantitative assessments based on known phantom characteristics.
• For clinical image evaluation: Expert Likert scale ratings of diagnostic image quality by radiologists were used. This is a form of expert consensus/opinion on diagnostic quality.

8. Sample Size for Training Set

The document does not specify a sample size for the training set. The descriptions focus on the evaluation of the ASiR-V algorithm, not its development or training data.

9. How Ground Truth for Training Set Was Established

As the document does not provide information about a training set or its sample size, it also does not specify how ground truth for a training set was established.

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The Veo reconstruction option is intended to produce cross-sectional images of the head and body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial and Helical (Volumetric) acquisitions for all ages.

When used, it allows for an alternate reconstruction method designed to reduce image noise, increase resolution and improve low contrast detectability in images produced using raw Computed Tomography data from GE CT scanners. The Veo reconstruction option can be used to reduce noise in diagnostic images and also to reduce the dose required for routine imaging, including CT scans of the head, chest, abdomen and pelvis. The Veo reconstruction option may also improve the image quality of low dose non-diagnostic Filtered Backprojection images such that they become diagnostic.

Currently, Veo is for use with the Discovery CT750 HD CT Scanner.

The Veo Reconstruction Option is composed of Server hardware and reconstruction software. The Veo reconstruction Server is connected to the CT system's operator console via a dedicated Ethemet connection to receive raw scan data for processing and send back the reconstructed image data when completed. Additionally, some minor changes to the CT system software are made to include functionality for installation and operation of the Veo Reconstruction Option. The option when used with the Discovery CT750 HD system (K081105) is an evolutionary modification and performs as well as or better than the computed tomography devices currently on the market. The product changes are primarily associated with the new reconstruction software and hardware. The Veo Reconstruction Option provides another method for reconstruction to that already provided by the CT system.

The Veo Reconstruction Option is intended for head and whole body CT scans when higher image quality and/or lower dose acquisitions are desired for challenging cases. The GE Veo Reconstruction Option when used with the CT System uses virtually the same materials and identical CT imaging principles as our existing marketed product. Discovery CT750 HD.

The provided document is a 510(k) summary for the GE Veo Reconstruction Option, which is an add-on to existing CT systems. It does not contain a detailed study report with specific acceptance criteria and performance metrics in the format requested. The document primarily focuses on establishing substantial equivalence to a predicate device and outlining the intended use and device description.

Therefore, I cannot provide a table of acceptance criteria and reported device performance, nor can I elaborate on sample sizes, ground truth establishment, expert qualifications, or multi-reader multi-case studies, as this information is not present in the provided text.

The document states: "The Veo Reconstruction Option when used with a GE CT system is an evolutionary modification and performs as well as or better than the computed tomography devices currently on the market." This suggests that the "acceptance criteria" were likely based on demonstrating that the Veo option did not degrade the performance of the CT system and potentially improved certain aspects like noise reduction, resolution, and low contrast detectability, compared to conventional reconstruction methods. However, specific quantitative acceptance criteria or detailed study results are not provided.

Here's what can be extracted and inferred from the text regarding the study and evaluation, acknowledging the significant gaps in detail:

1. Table of Acceptance Criteria and Reported Device Performance
This information is not explicitly provided in the document. The submission focuses on demonstrating substantial equivalence rather than reporting specific performance metrics against pre-defined acceptance criteria.

• Inferred Performance Claims (Qualitative):
  • Reduce image noise
  • Increase resolution
  • Improve low contrast detectability
  • May improve the image quality of low dose non-diagnostic Filtered Backprojection images such that they become diagnostic.
  • Allows for an alternate reconstruction method.
  • Performs as well as or better than the computed tomography devices currently on the market.

2. Sample size used for the test set and the data provenance
The document does not specify any sample size for a test set or data provenance (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 mentioned in the provided text.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not mentioned in the provided text.

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
The document does not describe an MRMC comparative effectiveness study or any effect size related to human reader improvement with or without AI assistance. The Veo Reconstruction Option is described as a reconstruction method, not specifically an AI algorithm with human-in-the-loop performance evaluation.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The document describes the device as a "reconstruction option" that processes raw CT data. It is implied that the performance of this reconstruction is evaluated in terms of image quality metrics (noise, resolution, low-contrast detectability) rather than a diagnostic algorithm's standalone performance on a specific task.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
Not mentioned in the provided text. Given the focus on image quality parameters (noise, resolution, contrast), the evaluation likely involved quantitative phantom studies and potentially qualitative assessment by experts on clinical images, but the method of establishing ground truth is not detailed.

8. The sample size for the training set
Not mentioned in the provided text. As this is a reconstruction option, the "training set" concept (as typically applied in machine learning) might not be directly applicable in the conventional sense, or if used, it is not disclosed.

9. How the ground truth for the training set was established
Not mentioned in the provided text.

Summary of what the document DOES state regarding the study/evaluation:

• Conclusion about Performance: "The Veo Reconstruction Option when used with a GE CT system is an evolutionary modification and performs as well as or better than the computed tomography devices currently on the market." (Page 3)
• Evaluation Methodology (Implied): The device was "developed under GE's Quality System" and "Functional requirements are demonstrated via testing." (Page 3). The claims around noise reduction, increased resolution, and improved low contrast detectability suggest that these were areas of focus for the testing.
• Relationship to Predicate: "The GE Veo Reconstruction Option when combined with the GE CT system is of comparable type and substantially equivalent to GE Healthcare's currently marketed Computed Tomography X-ray Systems." (Page 3). This "substantial equivalence" is the primary regulatory pathway demonstrated.
• Intended Benefits: The option is "designed to reduce image noise, increase resolution and improve low contrast detectability" and "can be used to reduce noise in diagnostic images and also to reduce the dose required for routine imaging." It "may also improve the image quality of low dose non-diagnostic Filtered Backprojection images such that they become diagnostic." (Page 3)

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The Brilliance CT is a Computed Tomography X-Ray System intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data taken at different angles and planes. This device may include signal analysis and display equipment, patient, and equipment supports, components and accessories.

The dual energy option allows the system to acquire two CT images of the same anatomical location using two distinct tube voltages and/or tube currents during two tube rotations. The x-ray dose will be the sum of the doses of each tube rotation at its respective tube voltage and current. Information regarding the material composition of various organs, tissues, and contrast materials may be gained from the differences in x-ray attenuation between these distinct energies. This information may also be used to reconstruct images at multiple energies within the available spectrum, and to reconstruct basis images that allow the visualization and analysis of anatomical and pathological materials.

Philips Healthcare offers a Dual Energy scanning option on the Brilliance CT Scanner. The Brilliance Dual Energy option automates the execution of sequential scanning protocols acquired during the same episode of care using two unique tube voltages and/or currents. The acquired datasets can be displayed side-by-side or overlaid and then analyzed to augment the review of anatomical and pathological structures. Dual energy imaging, by nature of differing x-ray energy values, enables the identification of attenuation differences found in those structures between the two applied energies.

This submission K090462 for the Philips Medical Systems (Cleveland) Inc. Brilliance Dual Energy option does not contain the detailed information necessary to fully address all aspects of your request regarding acceptance criteria and a study proving the device meets those criteria.

The document is a 510(k) Summary, which primarily focuses on establishing substantial equivalence to predicate devices and detailing the intended use. It does not typically include the specifics of performance studies, acceptance criteria, or ground truth establishment that would be found in a full submission or a clinical study report.

Based on the provided text:

• No specific acceptance criteria or a study demonstrating the device meets those criteria are explicitly reported. The document states that the device is "of comparable type and substantially equivalent to the legally marketed devices" (K060937 and K081105) based on "similar technological characteristics and subassemblies." This is a regulatory statement of equivalence, not a performance study result against stated acceptance criteria.

Therefore, I cannot populate the table or provide detailed answers to most of your questions based solely on the provided text.

However, I can extract information related to the device description and intended use:

1. Table of Acceptance Criteria and Reported Device Performance

Since the document does not describe a performance study with acceptance criteria, the following questions cannot be answered from the provided text:

• 2. Sample size used for the test set and the data provenance: Not mentioned.
• 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not mentioned.
• 4. Adjudication method for the test set: Not mentioned.
• 5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done: Not mentioned.
• 6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not mentioned, as this is an imaging option, not a standalone algorithm.
• 7. The type of ground truth used: Not mentioned.
• 8. The sample size for the training set: Not mentioned.
• 9. How the ground truth for the training set was established: Not mentioned.

Summary of what the document does provide:

• Device Name: Brilliance Dual Energy option
• Intended Use: To produce cross-sectional images using two distinct tube voltages and/or tube currents, aiding in material composition analysis, and reconstruction of images at multiple energies and basis images.
• Classification: Class II (21 CFR 892.1750, Product Code 90 JAK)
• Predicate Devices: Philips Brilliance Volume (K060937) and GE Lightspeed CT750 HD (K081105).
• Basis for Equivalence: Similar technological characteristics and subassemblies.

To obtain the detailed study information you're asking for, one would typically need access to the full 510(k) submission, not just the summary, or any publicly available performance reports or clinical studies related to this specific device option.

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