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The Philips IQon Spectral 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 parts, and accessories.

The IQon Spectral CT system acquires one CT dataset – composed of data from a higher-energy detected x-ray spectrum and a lower- energy detected x-ray spectramay be used to analyze the differences in the energy dependence of the attenuation coefficient of different materials. This allows for the generation of images at energies selected from the available spectrum and to provide information about the chemical composition of the body materials and/or contrast agents. Additionally, materials analysis provides 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 Philips IQon Spectral CT is a whole-body computed tomography (CT) X-Ray System featuring a continuously rotating x-ray tube and detectors gantry and multi-slice capability. The acquired x-ray transmission data is reconstructed by computer into cross-sectional images of the body taken at different angles and planes. This device also includes signal analysis and display equipment; patient and equipment supports; components; and accessories. The Philips IQon Spectral CT includes the detector array previously described in K131773 "Modified Brilliance iCT".

The IQon Spectral CT consists of three main components – a scanner system that includes a rotating gantry, a movable patient couch, and an operator console for control and image reconstruction; a Spectral Reconstruction System; and a Spectral CT Viewer. On the gantry, the main active components are the x-ray high voltage (HV) power supply, the x-ray tube, and the detection system.

In addition to the above components and the software operating them, the system includes workstation hardware and software for data acquisition; and image display, manipulation, storage, and filming, as well as post-processing into views other than the original axial images. Patient supports (positioning aids) are used to position the patient.

The Philips IQon Spectral CT is a Computed Tomography (CT) X-Ray System. The provided document is a 510(k) Summary, which describes the device and its intended use, and provides a summary of non-clinical and clinical testing to demonstrate substantial equivalence to a predicate device. However, it does not contain specific acceptance criteria, detailed study designs, or reported device performance metrics in the format requested, such as sensitivity, specificity, or AUC calculated from a clinical trial. The document focuses on regulatory compliance and the types of tests performed rather than the quantitative results against specific criteria.

Therefore, I cannot fully complete the requested table and answer all questions with the detailed information usually found in a clinical study report. I will extract what information is present.

Acceptance Criteria and Reported Device Performance

The provided document does not explicitly list quantitative acceptance criteria in a table format for specific performance metrics (e.g., sensitivity, specificity, accuracy) that would be typically established for a diagnostic device. It focuses on demonstrating conformance to standards and the utility of new spectral imaging capabilities.

Instead of specific acceptance criteria, the document states general conformance to standards and demonstrates capabilities:

Study Details (Based on available information)

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

   • Test Set Sample Size: Not explicitly stated in terms of number of patients or cases. The document mentions "Clinical images were collected and analyzed," but does not provide a specific count.
   • Data Provenance: Not explicitly stated (e.g., country of origin, retrospective/prospective). It is a "Summary of Clinical Testing," suggesting real-world clinical data was used, but details are absent.

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

   • Not specified. The document states "This evaluation demonstrated that spectral images were useful for the visualization and analysis of anatomical and pathological structures," implying evaluation by trained healthcare professionals, but no details on the number or qualifications of these experts are provided.

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

   • Not specified.

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:

   • The document describes the Philips IQon Spectral CT as a device for generating images and quantitative data, not an AI software/algorithm that assists human readers. Therefore, a multi-reader, multi-case (MRMC) comparative effectiveness study focusing on the improvement of human readers with AI assistance is not applicable to this device as described. The clinical testing mentioned focused on the utility of the spectral images themselves for visualization and analysis.

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

   • The IQon Spectral CT is a CT scanner system that produces images and performs material analysis. Its core function is image acquisition and reconstruction, and spectral capabilities like quantification of iodine, calcium, and effective atomic number are inherent features of the device's processing capabilities. It's not an "algorithm only" in the sense of a separate AI-driven diagnostic tool. The "performance" in this context refers to the system's ability to generate these specific types of images and quantitative data accurately and consistently, which was assessed through non-clinical (phantom-based) and clinical evaluation. The clinical evaluation primarily confirmed the usefulness of the generated spectral images.

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

   • For non-clinical testing, the ground truth appears to be established through the specifications of phantoms and reference measurements (e.g., assessing CT linearity, spatial resolution, low contrast detectability against known values).
   • For clinical testing, the "ground truth" for demonstrating usefulness in "visualization and analysis of anatomical and pathological structures" would typically involve comparison with existing diagnostic methods, expert interpretation of images, or correlation with pathology/clinical outcomes. However, the document does not specify the exact type of ground truth used for the clinical evaluation. It only states that the images were found "useful."

7. The sample size for the training set:

   • The document describes a CT System, not a machine learning algorithm that requires a "training set" in the conventional sense. The "training" for such a system would involve engineering and calibration using diverse data to ensure robustness across various patient anatomies and conditions, but this is not a "training set" as understood in AI/ML performance studies.

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

   • As this is not an AI/ML algorithm requiring a "training set" with ground truth labels in the typical sense, this question is not applicable here. The system's design and calibration are based on physical principles of CT imaging and spectral analysis.

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The "Brilliance iCT" 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 Brilliance iCT is a Whole Body Computed Tomography (CT) X-Ray System featuring a continuously rotating x-ray tube and detectors gantry and multi-slice capability. The acquired x-ray transmission data is reconstructed by computer into cross-sectional images of the body taken at different angles and planes. This device also includes signal analysis and display equipment; patient and equipment supports; components; and accessories.

The submitted text describes a 510(k) summary for the Brilliance iCT, a Computed Tomography (CT) X-Ray System. The summary focuses on demonstrating the substantial equivalence of a modified version of the Brilliance iCT to its predicate device, "Brilliance Volume," by highlighting technical characteristics and performance data.

Here's an analysis of the provided information, structured to address your specific questions. It's important to note that the document is a 510(k) summary for a CT scanner, not necessarily an AI-powered diagnostic device in the modern sense. Therefore, many of your questions related to AI-specific studies (like MRMC, training sets, ground truth for AI, etc.) are not directly applicable or detailed in this type of submission for a CT hardware modification.

Acceptance Criteria and Device Performance

The study primarily focuses on demonstrating that the modified Brilliance iCT continues to meet established performance standards for CT systems, particularly IEC 61223-3-5:2004. The acceptance criteria are implicit in compliance with this standard, which outlines specific acceptance tests for the imaging performance of CT X-ray equipment.

Table of Acceptance Criteria and Reported Device Performance:

Detailed Responses to Specific Questions:

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

   • See the table above. The acceptance criteria are based on compliance with IEC 61223-3-5:2004, and the performance is stated as meeting this standard for each listed measurement. A clinical evaluation also confirmed diagnostic image quality.

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

   • The document mentions "bench tests" for the technical performance measurements. These typically involve phantoms and laboratory setups, not human patient data in the sense of a clinical trial.
   • For the "Clinical evaluation," the sample size of cases/images is not specified.
   • The data provenance (country of origin, retrospective/prospective) is not mentioned for either bench tests or clinical 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):

   • For the technical performance measurements (CT Number, Noise, Resolution, etc.), the "ground truth" is established by physical measurement standards and the test methodology dictated by IEC 61223-3-5:2004, not by human experts.
   • For the "Clinical evaluation," it states "images... have been evaluated by a radiologist as being of diagnostic quality." The number is one radiologist, and their specific qualifications (e.g., years of experience) are not provided.

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

   • Given that only "a radiologist" is mentioned for the clinical evaluation, it implies no multi-reader adjudication method was used. For the technical bench tests, adjudication by human experts is not applicable.

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 study was done or reported. This submission pertains to a modification of a CT scanner's hardware (detection array and supporting software), not an AI-powered diagnostic tool in the sense of one that assists human readers. The clinical evaluation mentioned is a basic check for diagnostic quality of the reconstructed images.

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

   • This question is not applicable in the context of this device. The Brilliance iCT is a CT scanner, which provides images for human interpretation, not an algorithm providing a standalone diagnostic output. The "algorithm only" performance refers to the image reconstruction algorithm's output, which is then clinically evaluated by a radiologist. The technical bench tests assess the fundamental image quality parameters, which are (in a way) "standalone" performance metrics of the hardware and reconstruction.

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

   • For the technical performance measurements, the "ground truth" is based on the physical properties of phantoms and measurement standards defined by IEC 61223-3-5:2004.
   • For the clinical evaluation, the ground truth for "diagnostic quality" appears to be the subjective assessment of a single radiologist. This is not equivalent to pathology or outcomes data.

8. The sample size for the training set:

   • Not applicable / Not mentioned. This document describes the modification of a CT scanner hardware and its performance, not an AI algorithm that would typically require a training set in the sense of machine learning. The "supporting software" mentioned would be for image reconstruction and system control, not a learned model.

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

   • Not applicable / Not mentioned for the same reasons as point 8.

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The Philips Ingenuity Digital PET/CT System is a diagnostic imaging device that combines Positron Emission Tomography (PET) and X-ray Computed Tomography (CT) systems. The CT subsystem images anatomical crosssections by computer reconstruction of x-ray transmission data. The PET subsystem images the distribution of PET anatomy-specific radiopharmaceuticals in the patient. The PET/CT system is used for the purpose of detecting, localizing, diagnosing, staging, re-staging and follow-up for monitoring therapy response of various diseases in oncology, cardiology and neurology. The system is intended to image the whole body, heart, brain, lung, gastrointestinal, bone, lymphatic, and other major organs for a wide range of patient types, sizes, and extent of diseases. Both subsystems can also be operated as fully functional, independent diagnostic tools including application of the CT scanner for diagnosis and for use in radiation therapy planning.

The device is a hybrid diagnostic imaging system that combines Positron Emission tomography and X-ray computed tomography scanners that can be utilized in fixed installations. The device is comprised of the following system components/subsystems: Positron Emission Tomography (PET) scanner, X-ray Computed Tomography (CT) scanner, Patient Table

This Philips 510(k) Premarket Notification Submission for the Ingenuity Digital PET/CT System does not contain the detailed acceptance criteria and study results in the format requested.

The document primarily focuses on demonstrating substantial equivalence to a predicate device (K052640, Gemini Raptor). It states:

• Determination of Substantial Equivalence: "The Ingenuity Digital PET/CT System has comparable indications for use as its predicate device."
• Summary of Nonclinical Tests: "In accordance with "Guidance for the Submission of Premarket Notifications for Emission Computed Tomography Devices and Accessories (SPECT and PET) and Nuclear Tomography Systems", system performance is provided using NEMA NU-2. Safety evidence is provided using IEC 60601 series of standards."
• Summary of Clinical Tests: "In accordance with "Guidance for the Submission of Premarket Notifications for Emission Computed Tomography Devices and Accessories (SPECT and PET) and Nuclear Tomography Systems", clinical images are included."

These statements indicate that the submission referenced existing guidance documents (NEMA NU-2 and IEC 60601 for nonclinical, and a general guidance for clinical images) for performance and safety testing. However, it does not provide the specific acceptance criteria or the reported device performance in a table, nor does it detail the specifics of any studies (like sample sizes, ground truth establishment, expert qualifications, or MRMC studies).

Therefore, I cannot populate the table or answer the specific questions about sample sizes, ground truth, experts, and adjudication methods from the provided text. The document confirms that clinical images were included in the submission, but it doesn't describe a specific clinical study with detailed results for a new device's performance against defined acceptance criteria. It relies on the concept of substantial equivalence by meeting regulatory guidance and having similar indications for use to a previously cleared device.

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The IMR reconstruction feature is intended as an alternative to standard reconstruction methods (filtered back projection) for the reconstruction of CT scanner data to produce diagnostic images. The IMR reconstruction feature is designed to reduce image noise, increase high-contrast spatial resolution, and improve low contrast detectability. IMR is designed to reduce dose required for diagnostic CT imaging, Image quality improvements and dose reduction depend on the clinical task, patient size, anatomical location, and clinical practice. IMR images will be used by a trained medical professional for diagnosis of clinical conditions in patients, including pediatrics and adults, who have been prescribed a CT scan as part of their clinical care

The IMR reconstruction feature is intended as an alternative to standard reconstruction methods (filtered back projection) for the reconstruction of CT scanner data to produce diagnostic images. The IMR reconstruction feature is designed to reduce image noise, increase high-contrast spatial resolution, and improve low contrast detectability. IMR is designed to reduce dose required for diagnostic CT imaging. Image quality improvements and dose reduction depend on the clinical task, patient size, anatomical location, and clinical practice. The IMR Software Application will reside on any Philips CT System that meets minimum software platform and hardware requirements. IMR enables the user to apply iterative reconstruction techniques to reconstruct raw CT data to generate diagnostic CT images. The use of IMR to reconstruct images may be done prospectively or retrospectively.

Here's a breakdown of the acceptance criteria and study information for the Philips IMR Software Application, based on the provided text:

1. Acceptance Criteria and Device Performance

2. Sample Size and Data Provenance

• Test Set (Clinical Image Evaluation): 110 clinical image raw data sets.
• Data Provenance: Not explicitly stated (e.g., country of origin). The text refers to "clinical image raw data sets," implying retrospective data.

3. Number of Experts and Qualifications for Ground Truth (Clinical Image Evaluation)

• Number of Experts: A panel of eight physicians.
• Qualifications: Not explicitly stated beyond "physicians."

4. Adjudication Method (Clinical Image Evaluation)

• The text states: "Resulting data confirmed that the IMR software application provides diagnostic quality images and in majority of the cases, the physicians preferred the IMR images to standard (filter back projection) reconstruction." This suggests a comparative evaluation by the panel, likely involving subjective preference between FBP and IMR images, but a formal adjudication method like "2+1" or "3+1" is not specified.

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

• Was it done?: Yes, a "human observer study" was conducted for low contrast detectability (LCD), and a clinical image evaluation involving "a panel of eight physicians."
• Effect Size of human readers' improvement with AI vs. without AI assistance:
  • Low Contrast Detectability (4-AFC Detection human observer study): The resulting data "confirmed the improved LCD using the IMR application," with an improvement range of 43% - 80% relative to filtered backprojection, as demonstrated through phantom-based tests.
  • Clinical Image Evaluation: Physicians "preferred the IMR images to standard (filter back projection) reconstruction in the majority of the cases." No specific quantitative improvement (effect size) for human readers is given for this study beyond preference.

6. Standalone Performance (Algorithm Only)

• Was it done?: Yes, objective image quality testing was conducted using phantoms to determine noise, CT number uniformity, and high-contrast spatial resolution. The CT scan data was reconstructed by the IMR software application and compared to filtered backprojection. These are standalone performance metrics of the algorithm.

7. Type of Ground Truth Used

• Phantom-based tests: For noise, CT number uniformity, high-contrast spatial resolution, and low contrast detectability, phantom-based measurements were used as the ground truth, following methodologies like IEC 61223-3-5 and a 4-AFC detection human observer study with a low-contrast pin phantom.
• Clinical Image Evaluation: For the clinical image evaluation, the ground truth was based on the diagnosed quality and preferences of a panel of eight physicians, comparing IMR to standard (FBP) reconstructions. This is a form of expert consensus/subjective evaluation.

8. Sample Size for the Training Set

• The document does not provide information regarding the sample size used for training the IMR software application. The studies described are primarily for performance testing and validation.

9. How Ground Truth for the Training Set Was Established

• The document does not provide information on how ground truth was established for a training set, as it focuses on performance testing rather than development or training methodologies.

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The Philips Iterative Reconstruction Technique (IRT) Software Application is intended to reconstruct raw data from a Philips CT Scanner to produce images containing noise levels less than or equal to images produced by standard Filtered Back Projection reconstruction. Resulting IRT images are to be used to supplement conventional Filtered Back Projection images to aid the physician in diagnosis; they are not to be used as the sole basis for diagnosis.

The Philips IRT Software Application is a software option used for the reduction of noise in an image. IRT iteratively reconstructs raw data from a Philips CT Scanner to produce images containing noise levels less than or equal to images produced by standard Filtered Back Projection (FBP) reconstruction. This feature will be used by radiologists as a supplementary method to reconstruct CT raw data, in addition to traditional FBP.

Here's a breakdown of the acceptance criteria and the study information based on the provided text for the Philips Iterative Reconstruction Technique (IRT) Software Application:

1. Table of Acceptance Criteria and Reported Device Performance

Study Information

The provided text describes both non-clinical and clinical image data testing.

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

• Non-clinical testing (Objective Image Quality):
  • Test Set Description: Phantom raw CT scan data.
  • Sample Size: Not explicitly stated, but implies multiple phantom scans were reconstructed for comparison.
  • Data Provenance: Not specified, but generally phantom data is created in a controlled lab/testing environment.
• Non-clinical testing (Low Contrast Detectability - LCD):
  • Test Set Description: Images used for an observer study with a low contrast test object.
  • Sample Size: "The test was repeated multiple times for each test subject, and multiple test subjects were used." (Specific numbers not given).
  • Data Provenance: Not specified, but likely generated in a controlled testing environment.
• Clinical testing (Noise Reduction):
  • Test Set Description: Clinical image raw data sets.
  • Sample Size: Not explicitly stated.
  • Data Provenance: Not specified, but implied to be retrospective as it refers to existing "clinical image raw data sets."

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

• For the Low Contrast Detectability (LCD) observer study: "a cohort of human subjects was required to identify a low contrast test object..."

  • Number of experts: Not specified beyond "multiple test subjects."
  • Qualifications of experts: Not explicitly stated. It only mentions "human subjects," not necessarily radiologists or experts in image perception.

• For Objective Image Quality and Clinical Noise Reduction where ground truth is based on physical measurements or direct comparison, no external experts are mentioned for establishing ground truth.

4. Adjudication method for the test set:

• Objective Image Quality and Clinical Noise Reduction: No explicit human adjudication method is described for these. The comparison seems to be based on direct measurement (noise, CT number uniformity, high contrast spatial resolution) or visual comparison of noise levels by Philips.
• Low Contrast Detectability (LCD): The "observer study" served as the evaluation method, where human subjects (not explicitly "adjudicators" in the sense of resolving conflicting interpretations, but rather participants identifying objects) made determinations. No specific adjudication protocol (like 2+1 or 3+1) is mentioned for resolving differences among these subjects, as the data was likely used to characterize the statistical nature of detection.

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:

• A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly described in the document in the typical sense of measuring reader performance with and without AI assistance for diagnosis.
• An observer study was conducted for Low Contrast Detectability (LCD), which involves multiple human subjects. However, its stated purpose was to confirm improved LCD using IRT, not to quantify diagnostic improvement of human readers with IRT versus without it as an assistive tool to be compared against a human-only baseline. The IRT images are intended to "supplement conventional Filtered Back Projection images to aid the physician in diagnosis," suggesting an assistive role, but the study described doesn't measure this specific comparative effectiveness for diagnostic improvement.

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

• Yes, standalone performance was a primary focus.
  • The "Objective image quality testing" using phantoms evaluated the algorithm's performance (noise, CT number uniformity, high contrast spatial resolution) directly by comparing IRT reconstructions to FBP reconstructions.
  • The "Clinical image raw data sets" were also used to compare noise reduction between IRT and FBP.
  • The IRT software application is described as an "iterative reconstruction technique" which processes raw data independently to produce images.

7. The type of ground truth used:

• Objective Image Quality (Phantoms): Ground truth was established by the known physical properties and measurements from the phantoms, adhering to methodologies like IEC 61223-3-5. This is a form of physical/definitive measurement ground truth.
• Low Contrast Detectability (LCD): Ground truth was the verifiable presence or absence of the low contrast test object within the images. This is a form of definitive presence/absence ground truth.
• Clinical Noise Reduction: Ground truth for noise reduction was based on direct comparison of noise levels between IRT and FBP reconstructions of the same raw clinical data. This relies on comparative measurement ground truth against an established baseline (FBP).

8. The sample size for the training set:

• The document does not provide any information about the training set size or how the algorithm was trained. This 510(k) summary focuses on the verification and validation (testing) of the final product.

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

• As the document does not mention a training set, it does not provide information on how its ground truth was established.

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The device is a diagnostic imaging system for fixed installations that combines Positron Emission Tomography (PET) and Magnetic Resonance Iniaging (MRI). The system does not expose the patient to ionizing radiation, only the dose contribution from the PET radiopharmaceutical. The MRI subsystem produces cross-sectional images, spectroscopy images and/or spectra in any orientation of the internal structures of the whole body. The PET subsystem produces images of the distribution of PET radiopharmaceuticals in the patient body (specific pharmaceuticals are used for whole body, brain, and other organ imaging). The PET and MRI portions of the system can be used either as an integrated system or as a stand-alone MRI or PET system. The MRI subsystem provides data suitable for use in attenuation correction of the PET acquired data.

Image processing and display work stations provide software applications to process, analyze, display, quantify and interpret medical images/data via a single user interface. The PET and MRI images may be registered and displayed in a fused (overlaid in the same spatial orientation) format to provide combined metabolic and anatomical data at different angles. Trained professional use the images in:

• The evaluation, detection and diagnosis of lesions, disease, and organ function . such as but not limited to cancer, cardiovascular disease, and neurological disorders.
• . The detection, localization, and staging of tumors and diagnosing cancer patients.
• Treatment planning and interventional radiology procedures. ♥

The device includes software that provides a quantified analysis of regional cerebral activity from the PET images.

The MRI provides capabilities to perform interventional procedures in the head, body, and extremities which may be facilitated by MR techniques, such as real time imaging, such procedures must be performed with MRI compatible instrumentation as selected and evaluated by the clinical user.

The Ingenuity TF PET/MR system combines the Philips Achieva MR (K063559) and the Philips GEMINI PET/CT (Raptor, K052640) technologies. The system utilizes the MR technology to obtain anatomic images of the human body and PET technology to obtain functional images of the human body. The clinical value of the both technologies is enhanced with the ability to fuse the MR and PET images using Philips fusion viewer software to create a composite image for diagnostic study and therapeutic planning. The system also provides tools for the quantification of results of the MR and PET images and provides a means for a simplified review of the fused images.

Magnetic Resonance Imaging is a medical imaging technique that is based on the principle that certain atomic nuclei present in the human body emit a weak relaxation signal when placed in a strong magnetic field and excited by a radio signal at the precession frequency. The emitted relaxation signals are analyzed by the system and a computed image reconstruction is displayed on a video screen.

Positron Emission Tomography uses radiopharmaceuticals to obtain images by measuring the internal distribution of radioactivity within organs of the body. PET technology enables the practitioner to reconstruct high resolution, three dimensional images of biochemical and metabolic processes of organs within the body.

The Ingenuity TF PET/MR system consists of two gantries integrating the MR scanner and the PET scanner, a patient table to support the patient, within the gantries, and a scanning console and viewing console at the operator's workstation.

The request asks for specific information regarding the acceptance criteria and study proving device meets acceptance criteria, based on the provided text. However, the provided text is a 510(k) summary for a PET/MR system, which focuses on device description, intended use, and substantial equivalence to predicate devices, rather than detailed performance studies or specific acceptance criteria for AI algorithms.

Therefore, much of the requested information cannot be extracted from the given document as it pertains to AI/algorithm performance studies which are not described.

Here's what can be extracted:

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

The document does not explicitly state quantitative acceptance criteria or detailed performance metrics for an AI algorithm. It mentions:

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

• Sample Size for Test Set: Not specified.
• Data Provenance: "Clinical data" is mentioned, implying real patient data, but details such as country of origin, retrospective or prospective nature are not provided.

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 specified in the document.

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

Not specified 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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study is not mentioned. The document primarily focuses on demonstrating substantial equivalence of the new PET/MR system to existing predicate devices (PET/CT and MRI) based on image quality and the MRAC method, not on human reader performance with or without AI assistance.

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

This document describes a diagnostic imaging system, not a standalone AI algorithm. The performance evaluation mentioned ("acceptable image quality" and "MRAC method was validated") refers to the system as a whole.

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

For the MRAC method, the ground truth was established through "phantom, simulated and clinical data." The specific nature of "ground truth" for image quality equivalence is not detailed but would typically involve visual assessment by experts against established standards or predicate images.

8. The sample size for the training set

Not applicable. The document describes a medical imaging system, not a device whose performance is based on an AI algorithm that requires a training set.

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

Not applicable.

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