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The AV Viewer is an advanced visualization software intended to process and display images and associated data in a clinical setting.

The software displays images of different modalities and timepoints, and performs digital image processing, measurement, manipulation, quantification and communication.

The AV Viewer is not to be used for mammography.

AV Viewer is an advanced visualization software which processes and displays clinical images from the following modality types: CT, CBCT – CT format, Spectral CT, MR, EMR, NM, PET, SPECT, US, XA (iXR, DXR), DX, CR and RF.

The main features of the AV Viewer are:
• Viewing of current and prior studies
• Basic image manipulation functions such as real-time zooming, scrolling, panning, windowing, and rolling/rotating.
• Advanced processing tools assisting in the interpretation of clinical images, such as 2D slice view, 2D and 3D measurements, user-defined regions of interest (ROIs), 3D segmentation and editing, 3D models visualization, MPR (multi planar Reconstructions) generation, image fusion and more.
• A finding dashboard used for capturing and displaying findings of the patient as an overview.
• Customized workflows allow the user to create their own workflows
• Tools to export customizable reports to the Radiology Information System (RIS) or PACS (Picture archiving and communication system) in different formats.

AV Viewer is based on the AV Framework, an infrastructure that provides the basis for the AV Viewer and common functionalities such as: image viewing, image editing tools, measurements tools, finding dashboard.

AV viewer can be hosted on multiple platforms and devices, such as Philips AVW, Philips CT/MR scanner console or on cloud.

The provided FDA 510(k) clearance letter for the AV Viewer device indicates that the device has met its acceptance criteria through various verification and validation activities. However, the document does not include detailed quantitative acceptance criteria (e.g., specific thresholds for accuracy, sensitivity, specificity, or measurement error) or comprehensive performance data that would typically be presented in a clinical study report. The submission focuses on demonstrating "substantial equivalence" to a predicate device rather than presenting detailed performance efficacy of the algorithm itself.

Therefore, much of the requested information regarding specific performance metrics, sample sizes for test and training sets, expert qualifications, and detailed study methodologies is not explicitly stated in this 510(k) summary. I will extract and infer what is present and explicitly state when information is missing.

Here's a breakdown based on the provided document:

Acceptance Criteria and Device Performance

The document describes comprehensive verification and validation activities, including "Bench Testing" for measurements and segmentation algorithms. However, specific quantitative acceptance criteria (e.g., "accuracy > 95%") and the reported performance values are not detailed in this summary. The general statement is that "Product requirement specifications were tested and found to meet the requirements" and "The validation objectives have been fulfilled, and the validation results provide evidence that the product meets its intended use and user requirements."

Table of Acceptance Criteria and Reported Device Performance

Note: The quantitative details for the "Acceptance Criteria" and "Reported Device Performance" for measurement accuracy and segmentation accuracy are missing from this 510(k) summary. The document only confirms that these tests were performed and the results were positive.

Study Details Based on the Provided Document:

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

• Test Set Sample Size: Not explicitly stated. The document mentions "Verification," "Validation," "Expert Test," and "Bench Testing" were performed, implying the use of test data, but no specific number of cases or images in the test set is provided.
• Data Provenance: Not explicitly stated. The document does not specify the country of origin of the data used for testing, nor does it explicitly state whether the data was retrospective or prospective.

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

• Number of Experts: Not explicitly stated. The "Expert Test" mentions "a clinical expert" (singular) was used to test use case scenarios. It does not mention experts establishing ground truth for broader validation.
• Qualifications of Experts: The "Expert Test" mentions "a clinical expert." For intended users, the document states "trained professionals, including but not limited to, physicians and medical technicians" (Subject Device), and "trained professionals, including but not limited to radiologists" (Predicate Device). It can be inferred that the "clinical expert" would hold one of these qualifications, but specific details (e.g., years of experience, subspecialty) are not provided.

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

• Adjudication Method: Not explicitly stated. The document refers to "Expert test" where "a clinical expert" tested scenarios, implying individual assessment rather than a multi-reader adjudication process for establishing ground truth for a test set.

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 Comparative Effectiveness Study: Not explicitly stated or implied. The document focuses on the device's substantial equivalence to a predicate device and its internal verification and validation. There is no mention of a human-in-the-loop MRMC study to compare reader performance with and without AV Viewer assistance. The AV Viewer is described as an "advanced visualization software" and not specifically an AI-driven diagnostic aid that would typically warrant such a study for demonstrating improved reader performance.

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

• Standalone Performance Study: The "Bench Testing" section states that it "was performed on the measurements and segmentation algorithms to validate their performance and the correctness of the various measurement functions." This implies a standalone evaluation of these specific algorithms. However, the quantitative results (e.g., accuracy, precision) of this standalone performance are not provided.

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

• Type of Ground Truth: For the "Bench Testing" of measurement and segmentation algorithms, the ground truth would likely be based on reference measurements/segmentations, possibly done manually by experts or using highly accurate, non-clinical methods. For other verification/validation activities, the ground truth would be against the pre-defined product and user requirements. However, explicit details about how this ground truth was established (e.g., expert consensus, comparison to gold standard devices/methods) are not specified.

8. The sample size for the training set

• Training Set Sample Size: Not explicitly stated. The document does not mention details about the training data used to develop the AV Viewer's algorithms. The focus is on validation for regulatory clearance. Since the product is primarily an "advanced visualization software" with general image processing tools, much of its functionality might not rely on deep learning requiring large, distinct training sets in the same way a specific AI diagnostic algorithm would.

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

• Ground Truth for Training Set: Not explicitly stated. As no training set details are provided, the method for establishing its ground truth is also not mentioned.

Summary of Missing Information:

This 510(k) summary provides a high-level overview of the device's intended use, comparison to a predicate, and the types of verification and validation activities conducted. It largely focuses on demonstrating "substantial equivalence" based on similar indications for use and technological characteristics. Critical quantitative details about the performance of specific algorithms (measurements, segmentation), the size and characteristics of the datasets used for testing, and the methodology for establishing ground truth are not included in this public summary. Such detailed performance data is typically found in the full 510(k) submission, which is not publicly released in its entirety.

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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.

• 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.

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