(42 days)
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.
| Acceptance Criteria | Reported Device Performance (Summary from text) |
|---|---|
| Compliance with International and FDA Recognized Consensus Standards | Non-clinical performance testing demonstrates compliance with: |
- IEC 60601-1:2005 (Third Edition) + CORR. 1:2006 + CORR. 2:2007 + A1:2012
- IEC 60601-1-2:2014
- IEC 60601-1-3:2008+A1:2013
- IEC 60601-1-6:2010 +A1: 2013
- IEC 60601-2-44:2009/AMD2:2016
- IEC 62304:2006 + A1: 2015
- ISO 10993-1:2009/Cor.1:2010
- ISO 14971 2nd Edition. |
| Compliance with Device Specific Guidance Documents | Non-clinical performance testing demonstrates compliance with: - Guidance for Industry and FDA Staff - Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" (May 11, 2005)
- Content of Premarket Submissions for Management of Cybersecurity in Medical Devices (October 2, 2014). |
| Meeting established system and sub-system level design input requirements | Design Verification planning and testing was conducted at sub-system and system levels; activities demonstrate system/sub-systems meet requirements. |
| Image Quality Verification | Included in Design Verification; "Sample clinical images were provided... reviewed and evaluated by certified radiologists. All images were evaluated to have good image quality." |
| Risk Analysis and Mitigation | Risk analysis risk mitigation testing included in Design Verification. Traceability Matrix links requirements, hazard mitigations, and test protocols. |
| Usability and Clinical Workflow Validation for intended use and commercial claims | Non-Clinical design validation testing covered intended use and commercial claims as well as usability testing with representative intended users, including clinical workflow validation and service validation. |
| Demonstration of Substantial Equivalence to Predicate Device (K163711) in Safety and Effectiveness | Demonstrated through: Indication for use (updated statement not introducing new risk), Technological characteristics (identical fundamental scientific technology), Non-clinical performance testing (compliance with standards), and Safety and effectiveness (as safe/effective as predicate). |
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.
§ 892.1750 Computed tomography x-ray system.
(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.