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The 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 supports, component 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 spectra may be used to analyse 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 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 and to be used for diagnostic imaging in radiology, interventional radiology, and cardiology and in oncology as part of treatment preparation and radiation therapy planning.

The system is also intended to be used for low dose CT lung cancer screening for the early detection of lung nodules that may represent cancer*.

The screening must be performed within the established inclusion criteria of programs / protocols that have been approved and published by either a governmental body or professional medical society.

*Please refer to clinical literature, including the results of the National Lung Screening Trial (N Engl. J Med 2011; 365:395-409) and subsequent literature, for further information.

The proposed IQon Spectral CT System 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 proposed IQon Spectral CT System includes the detector array, which is identical to the currently marketed and predicate device – Philips IQon Spectral CT System (K163711).

The proposed IQon Spectral CT System consists of three main components, that are identical to the currently marketed and predicate device. Philips IQon Spectral CT System (K163711) - 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 proposed IQon Spectral CT System includes workstation hardware and software for data acquisition; image display, manipulation, storage, and filming, as well as post-processing for views other than the original axial images. Patient supports (positioning aids) are used to position the patient.

The provided text is a 510(k) summary for the Philips IQon Spectral CT system. It states that the device is substantially equivalent to a previously cleared predicate device (K163711) and describes non-clinical performance and a "change of indication for use statement and minor modifications." Crucially, this document explicitly states, "The proposed IQon Spectral CT system did not require any external clinical study."

Therefore, many of the requested details regarding acceptance criteria for an AI/CADe device performance study, sample sizes for test sets, expert ground truth establishment, MRMC studies, and standalone performance tests are not applicable in this context. This 510(k) is for a CT scanner itself and highlights updates to its indications for use and minor modifications, not for a new AI/CADe algorithm requiring specific clinical performance evaluation as described in the prompt.

However, based on the information provided, I can infer the "acceptance criteria" for the device itself and how the non-clinical performance demonstrates it meets those criteria, as detailed in the document.

Here's an interpretation based on the provided text, addressing the prompt as best as possible given the nature of the submission (a CT scanner, not an AI model):

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

Since this is a submission for a CT scanner and not an AI/CADe device with specific performance metrics like sensitivity/specificity for disease detection, the "acceptance criteria" relate to safety, effectiveness, and compliance with standards.

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

• Test Set Sample Size: Not explicitly stated as a number of patient cases or images, as this was a non-clinical evaluation focusing on system performance and compliance, not a clinical trial for an AI/CADe's diagnostic accuracy. The document mentions "Sample clinical images were provided," but not the quantity, provenance, or whether they constituted a standardized "test set" in the sense of an algorithm performance evaluation.
• Data Provenance: Not specified. Given it's a CT scanner, images would likely be from existing clinical data or phantom studies. The document mentions "Sample clinical images were provided," but doesn't detail their origin (e.g., country, retrospective/prospective).
• Retrospective or Prospective: Unspecified, but likely retrospective for image evaluation.

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

• Number of Experts: Not specified.
• Qualifications of Experts: "Certified radiologists" were used to evaluate image quality. No further details on experience or specialization are provided within this document.
• Establishment of Ground Truth: For image quality, the radiologists' evaluation of "good image quality" served as the assessment. For the system's overall safety and effectiveness, compliance with standards and internal testing served as the primary proof, rather than establishing a diagnostic "ground truth" for disease as would be needed for an AI algorithm.

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

Not applicable as there was no formal "test set" in the context of an AI/CADe performance study requiring ground truth adjudication. The radiologists' image quality evaluation method is not detailed.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

No. The document explicitly states: "The proposed IQon Spectral CT system did not require any external clinical study." Therefore, no MRMC study comparing human readers with and without AI assistance was performed or reported here.

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

Not applicable. This is for the CT scanner hardware/software system, not a standalone AI algorithm. The image reconstruction and analysis features like "Electron Density" and "Calcium Suppression Index" are integrated capabilities of the CT system, not separate AI algorithms undergoing standalone performance evaluation for diagnostic accuracy.

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

For image quality, the "ground truth" was based on the qualitative assessment of "good image quality" by certified radiologists. For system compliance, the "ground truth" was the defined requirements of international standards and internal specifications, tested through verification and validation activities. No pathology or outcomes data ground truth for disease diagnosis was required or used in this submission as it's not for an AI diagnostic aid.

8. The sample size for the training set

Not applicable. This document describes a CT scanner system, not a machine learning model that requires a training set. The descriptions of "Electron Density" and "Calcium Suppression Index" involve dedicated algorithms, but no details of training data for these are provided, nor would they typically be detailed in this type of submission for established image processing techniques in a CT scanner.

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

Not applicable, as there is no mention of a training set for an AI model.

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The 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 supports, component 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 spectra may 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 system is also intended to be used for low dose CT lung cancer screening for the early detection of lung nodules that may represent cancer*.

The screening must be performed within the established inclusion criteria of programs / protocols that have been approved and published by either a governmental body or professional medical society.

*Please refer to clinical literature, including the results of the National Lung Screening Trial (N Engl J Med 2011; 365:395-409) and subsequent literature, for further information.

The 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 xray transmission data is reconstructed by computer into crosssectional 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 IQon Spectral CT includes the detector array previously described in K133674 "Philips IQon Spectral CT".

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.

This document, a 510(k) Pre-Market Notification for the Philips IQon Spectral CT (K163711), indicates that no new clinical study was required to demonstrate substantial equivalence for the core device to its predicate (K133674). The addition of "Low Dose CT Lung Cancer Screening" to the Indications for Use was supported by a comparative assessment to another cleared device (K153444), rather than a de novo clinical study for the IQon Spectral CT itself.

Therefore, much of the requested information about acceptance criteria for a specific study conducted for this device, sample sizes for test sets, ground truth establishment, expert qualifications, and MRMC studies, is not present in this document as it pertains to this specific 510(k) submission for the IQon Spectral CT. The document relies heavily on the substantial equivalence to previously cleared devices.

However, I can extract information related to the general acceptance of the device based on non-clinical testing and its reliance on previously cleared predicate devices.

Acceptance Criteria and Device Performance (Based on Non-Clinical Data and Substantial Equivalence)

Study Details (as inferable from the document):

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

   • This 510(k) relies on non-clinical verification and validation testing, and comparative assessment to predicate devices, rather than a new clinical study. Therefore, there is no "test set" in the context of a new prospective or retrospective clinical study of patients, or information on data provenance, within this specific submission. The non-clinical tests would have involved phantom or laboratory testing.

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 applicable, as no new clinical test set requiring expert ground truth establishment is described in this 510(k). The "ground truth" for compliance testing would be defined by the standards and technical specifications being verified.

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

   • Not applicable, as no new clinical test set is described.

4. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • No MRMC study is mentioned or appears to have been conducted for this 510(k) submission. The device is a CT scanner, not an AI-assisted diagnostic tool in the typical sense that would necessitate an MRMC study for improved reader performance for this submission. The "minor improvements to some spectral algorithms" are internal technical refinements, not standalone AI algorithms for interpretation.

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

   • Not applicable. The IQon Spectral CT is a medical imaging device. Its function involves acquiring and processing images for interpretation by a human healthcare professional. There isn't a standalone algorithm performance evaluation described independent of the imaging system. The modifications mentioned ("minor improvements to some spectral algorithms") are part of the image generation/processing pipeline, not a separate diagnostic algorithm.

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

   • For the non-clinical performance data, the ground truth would be the established specifications and requirements defined by the international and FDA-recognized consensus standards (e.g., image quality metrics, dose measurements in phantoms, electrical safety parameters) and the technical design requirements of the device.
   • For the Low Dose CT Lung Cancer Screening indication, the ground for clinical validity would stem from the clinical literature referenced (e.g., National Lung Screening Trial) and the established inclusion criteria/protocols of governmental/professional medical societies, which inherently rely on outcomes data and pathology for nodule malignancy. The IQon Spectral CT's role is to provide the imaging data.

7. The sample size for the training set:

   • Not applicable. This document describes a medical device clearance based on substantial equivalence and non-clinical testing, not a machine learning model requiring a training set. The "spectral algorithms" receiving "minor improvements" are likely image reconstruction and processing algorithms that have been refined based on engineering principles and potentially internal validation, not AI models trained on large datasets.

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

   • Not applicable, as no training set for a machine learning model is described in this 510(k) submission.

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