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The Precise Image is a reconstruction software application for a Computed Tomography X-Ray System intended to produce images of the head and body by computer reconstruction of x-ray transmission data taken at different angles and planes. These devices may include signal analysis and display equipment supports, components and accessories. Precise Image has been evaluated and available on preselected reference protocols for adult subjects. Precise Image is not indicated for use in pediatric subjects.

The CT system with Precise Image is indicated for head, whole body and vascular X-ray Computed Tomography applications. These scanners are intended to be used for diagnostic imaging.

Precise Image uses an Artificial Intelligence powered reconstruction that is designed for low radiation dose, provides lower noise, and improves low contrast detectability.

The proposed Precise Image is a reconstruction software application that may be used on a Philips whole-body computed tomography (CT) X-Ray System. Precise Image is a robust reconstruction software application, utilizing technological advancements in Artificial Intelligence and a Convolutional Neural Networks (CNN), When used, Precise Image generates CT images that provides an image appearance similar to traditional FBP images while reducing dose and improving image quality.

The implemented algorithm includes 5 user-adjustable settings to match the Radiologist's preference for dose reduction and image quality.

The proposed Precise Image reconstruction has been trained on and may be used on the currently marketed predicate device Philips Incisive CT System (K180015).

Here's a breakdown of the acceptance criteria and the study details for the Philips Precise Image device, based on the provided text:

Acceptance Criteria and Device Performance

Study Details

1. Sample Size for Test Set and Data Provenance:

   • Sample Size: 55 image set pairs.
   • Data Provenance: The document states "Sample clinical images are provided with this submission," implying these are real clinical images. No specific country of origin is mentioned, nor is it explicitly stated if the data is retrospective or prospective. However, given they are "clinical images" and used for evaluation, it's highly likely they are retrospective images from existing clinical practice.

2. Number of Experts and Qualifications:

   • Number of Experts: 6 board-certified radiologists.
   • Qualifications: "board certified radiologists." No specific years of experience or subspecialty are provided.

3. Adjudication Method for the Test Set:

   • The document implies individual evaluations by each of the 6 radiologists on a Likert scale for various image attributes. It does not mention any explicit adjudication method (like 2+1 or 3+1 consensus) for the ground truth of the test set itself. The radiologists assessed "Diagnostic Confidence. Sharpness, Noise level. Image texture and Artifacts." The study compares the proposed device images against predicate device images, with the radiologists providing their individual assessment on these attributes.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • Was it done? Yes, a comparative image evaluation study was performed by 6 board-certified radiologists on 55 image set pairs.
   • Effect size of human readers improvement with AI vs without AI assistance: The document states that the evaluation was to "evaluate Diagnostic Confidence. Sharpness, Noise level. Image texture and Artifacts on a five point Likert scale" demonstrating "substantial equivalence to the currently marketed predicate device Philips Incisive CT (K180015)." It highlights improvements in low contrast resolution, noise reduction, and low contrast detectability of the device itself compared to the predicate/standard mode, but does not quantify human reader improvement (e.g., AUC, sensitivity, specificity) with AI assistance versus without it. The evaluation focused on image quality and characteristics, not diagnostic accuracy changes for the human reader.

5. Standalone (Algorithm Only) Performance Study:

   • Yes, the performance characteristics like "Low Contrast Resolution," "Noise Reduction and Low Contrast Detectability," and "Noise Power Spectrum" are measurements of the algorithm's output (the reconstructed image) and are done in a standalone manner without human intervention influencing these specific metrics. The clinical image evaluation by radiologists also assesses the output of the algorithm relative to the predicate.

6. Type of Ground Truth Used:

   • For the quantitative technical specifications (e.g., low contrast resolution, noise, NPS), the ground truth is based on phantom measurements (e.g., "20 cm Catphan phantom," "21.6 cm water-equivalent").
   • For the clinical image evaluation, the "ground truth" for comparison is the predicate device's images (Incisive CT and Brilliance iCT), with radiologists evaluating the attributes of the Precise Image compared to these established images. There is no mention of a separate, definitive, clinical ground truth (e.g., pathology, clinical outcomes) for the diagnosis from these images. The radiologists are evaluating image quality characteristics and comparing them.

7. Sample Size for the Training Set:

   • Not explicitly stated in the provided text. The document mentions, "The proposed Precise Image reconstruction has been trained on and may be used on the currently marketed predicate device Philips Incisive CT System (K180015)." However, it doesn't give a specific number of images or cases used for training.

8. How the Ground Truth for the Training Set was Established:

   • Not explicitly stated in the provided text. It mentions the device "has been trained on" the predicate device's data, implying that the established high-quality images from the predicate device likely served as a reference or ground truth for the AI training process to guide the AI in producing similar or improved image characteristics. However, the specific method of ground truth establishment for training data is not detailed.

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The 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 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 in patients of all ages, and to be used for diagnostic imaging in radiology, interventional radiology, and cardiology 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 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 Spectral CT System includes a detector array, which has spectral capability same as the cleared to market predicate device - Philips IQon Spectral CT System (K193454).

The proposed Spectral CT System consists of main components that are similar to the cleared to market predicate device, Philips IQon Spectral CT cleared under (K193454):
➤ Gantry -
On the rotating gantry, the main active components are:
• x-ray high voltage (HV) power supply,
• the x-ray tube,
• detection system
➤ Patient couch
➤ Operator console for control
➤ Common Image Reconstruction Unit (CIRS)
In addition to the above components and the operating software, 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.
• Spectral Reconstruction System
• Spectral CT Viewer.

This document is a 510(k) summary for the Philips Spectral CT system (K203020). It seeks to demonstrate substantial equivalence to a predicate device, the Philips IQon Spectral CT (K193454).

Here's an analysis of the provided information regarding acceptance criteria and supporting studies:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present acceptance criteria in a quantitative table with specific performance metrics (e.g., sensitivity, specificity, accuracy thresholds). Instead, the acceptance criteria are generally described as compliance with consensus standards and guidance documents, and meeting design input requirements.

2. Sample Size Used for the Test Set and Data Provenance

The document explicitly states: "There was no clinical testing conducted for the submission." This means there is no "test set" in the sense of patient data used to evaluate device performance on clinically relevant outcomes. The testing described is non-clinical performance testing.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

Since no clinical testing was performed and therefore no clinical "test set" was used, there were no experts used to establish ground truth from patient data.

4. Adjudication Method for the Test Set

Not applicable, as no clinical test set was used.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

No. The document explicitly states: "There was no clinical testing conducted for the submission." Therefore, no MRMC study comparing human readers with and without AI assistance was performed.

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

The device is a Computed Tomography X-Ray System, not a standalone AI algorithm. The performance evaluation focuses on the system's ability to acquire and reconstruct images and provide spectral analysis, not on an algorithm's diagnostic performance without human input. The "spectral results for cardiac" improvement is an algorithmic modification, but its performance is assessed as part of the overall system's technical capabilities and compliance with standards, not as a standalone diagnostic tool.

7. The Type of Ground Truth Used

For the non-clinical performance testing, the "ground truth" or reference was based on:

• Engineering specifications and design input requirements.
• International and FDA-recognized consensus standards (e.g., IEC 60601 series, ISO 14971).
• FDA guidance documents.
• The performance and characteristics of the predicate device (Philips IQon Spectral CT K193454).

8. The Sample Size for the Training Set

Not applicable. This submission is for a CT scanner system, not a machine learning model that requires a "training set" of data for its core functionality as described. While there are "Software life-cycle processes" compliant with IEC 62304, these relate to the overall development and verification of the software components of the CT system, not the training of a predictive AI model from a distinct dataset. The mention of "modification of the previously cleared classification method to target calcified structures" within the "Improved Spectral results" does imply some form of algorithm (or "classification method") development, but the document does not provide details on a specific training set size for this, nor does it present this as a primary subject of the 510(k) submission requiring clinical validation with a training set.

9. How the Ground Truth for the Training Set Was Established

Not applicable, as there's no defined "training set" in the context of this 510(k) summary for a CT system. Any underlying algorithms for spectral analysis would have had their "ground truth" derived from physics principles, material science, and possibly phantoms or validated datasets, but these details are not provided as part of this regulatory submission.

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