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The Philips iCT CT System is 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, patient and equipments and accessories. The iCT is indicated for head, whole body, cardiac and vascular X-ray Computed Tomography applications in patients of all ages.

These scanners are intended to be used for diagnostic imaging and 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 Philips iCT is currently available in two system configurations, iCT and iCT SP. Identical to the predicate, the Philips iCT CT System produces cross-sectional images of the body head and body by computer reconstruction of x-ray transmission data taken at different angles and planes. The main components (detection system, the reconstruction algorithm, and the x-ray system) that are used in the Philips iCT have the same fundamental design characteristics and are based on comparable technologies as the predicate.

The main system modules and functionalities are:

1. Gantry. The Gantry has an aperture of 700mm and consists of the following internal units:
   a. Stator a fixed mechanical frame that carries hardware and software.
   b. Rotor A rotating circular stiff frame that is mounted in and supported by the stator.
   c. X-Ray Tube (XRT) and Generator fixed to the Rotor frame. The generator has a power rating of 100kW with optional 120kW.
   d. Data Measurement System (DMS) a detectors array, fixed to the rotor frame. The DMS provides 8cm of coverage (4cm for the iCT SP configuration) and up to 256 slices (128 slices for the iCT SP configuration).The gantry offers 0.3 second rotation time (with optional 0.27s rotation).
2. Patient Table (aka Couch or Support) carries the patient in and out through the Gantry bore synchronized with the scan. There are three available patient supports:
   a. Standard Table provides maximum scannable range of 1750mm, longitudinal speed of 0.5mm/s-185mm/s and a maximum load capacity of 450 lbs.(204kg)
   b. Bariatric Table provides maximum scannable range of 1750mm, longitudinal speed of 0.5mm/s-185mm/s and a maximum load capacity of 650 lbs.(295kg)
   c. Extended Table provides maximum scannable range of 2100mm, longitudinal speed of 0.5mm/s-185mm/s and a maximum load capacity of 450 lbs.(204kg)
3. Console A two part subsystem containing a Host computer and display that is the primary user interface and the Common Image Reconstruction System (CIRS) - a dedicated powerful image reconstruction computer.
4. Monitors
5. Software features to view and analyze images.

This document is a 510(k) premarket notification for the Philips iCT CT System, which is a Computed Tomography X-Ray System. The document details the device's indications for use, description, and a comparison with a predicate device (Philips Brilliance Volume) to establish substantial equivalence.

Based on the provided text, the Philips iCT CT System is a Computed Tomography (CT) X-Ray System. The document outlines that the system is intended to produce images of the head and body by computer reconstruction of x-ray transmission data. It also explicitly states its indication for "low dose CT lung cancer screening for the early detection of lung nodules that may represent cancer."

Regarding acceptance criteria and the study proving the device meets these criteria:

The document focuses on demonstrating substantial equivalence to a predicate device (Philips Brilliance Volume) rather than presenting a performance study with explicit acceptance criteria for a novel AI/ML-driven diagnostic device. This means the 510(k) submission primarily relies on comparing the design, technology, and specified performance parameters of the new device to an already legally marketed device, and showing that any differences do not raise new questions of safety or effectiveness.

Therefore, many of the requested elements for an AI/ML diagnostic device study (like sample size for test sets, expert adjudication, MRMC studies, standalone performance with specific metrics like sensitivity/specificity, or ground truth establishment for novel findings) are not detailed or applicable in the traditional sense for this 510(k) submission. This submission is for hardware (CT scanner) with associated software for image reconstruction, not primarily a sophisticated AI/ML diagnostic algorithm operating on those images for making clinical decisions beyond image acquisition and display.

Here's an attempt to address the requested information based on the provided document:

1. Table of acceptance criteria and the reported device performance:

Since this is a 510(k) for a CT system demonstrating substantial equivalence, the "acceptance criteria" are implicitly met by showing that the proposed device's characteristics are either identical to or comparable to the predicate device, and any changes do not adversely affect safety or effectiveness. The document presents a comparative table, not a table of specific numerical performance acceptance criteria for a diagnostic algorithm.

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

The document mentions "Design Verification planning and testing was conducted at the sub-system and at the system level." It also states "Design validation of user needs and intended use was conducted via simulated use testing with production equivalent Philips iCT CT Systems."

• Test Set Sample Size: Not specified in terms of patient data or clinical cases for diagnostic performance. The document refers to "system and sub-system level verification" and "simulated use testing." This suggests engineering and functional testing rather than a large-scale clinical performance study on a specific number of patient scans.
• Data Provenance: Not specified. Given it's a Philips product for a global market, the data provenance for engineering/simulated testing is likely internal R&D, not necessarily specific patient data sets from particular countries. "Retrospective or prospective" is not applicable in the context of hardware/software functional testing described.

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

Not applicable. This 510(k) is not for an AI/ML diagnostic algorithm that requires expert-established ground truth on clinical images for its performance evaluation. The "ground truth" for a CT system's performance in this context would be its physical specifications, image quality metrics (like spatial resolution, contrast resolution, noise), and safety parameters (radiation dose). These are typically verified against engineering specifications, phantoms, and regulatory standards, not clinical ground truth established by experts.

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

Not applicable. No clinical image or diagnostic performance adjudication is described.

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:

Not applicable. This submission is for a CT hardware system, not an AI-assisted diagnostic tool that integrates with human readers. The "low dose CT lung cancer screening" indication refers to the capability of the scanner, and refers to existing clinical literature (National Lung Screening Trial) as evidence for the efficacy of low-dose CT screening in general, not specific AI augmentation.

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

Not applicable for a diagnostic algorithm. The device, the Philips iCT CT System, is the standalone imaging system for image acquisition and reconstruction. Its performance is evaluated against engineering specifications and industry standards.

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

The "ground truth" for the device's technical performance (e.g., image quality, scan range, rotation times) would be established by:

• Engineering specifications: The designed parameters of the CT system components.
• Physical phantoms: Standardized objects used to measure image quality characteristics (e.g., spatial resolution, contrast-to-noise ratio, slice thickness accuracy).
• Calibration procedures: Ensuring the system's measurements are accurate.
• Compliance with international standards: (e.g., IEC 60601 series for medical electrical equipment safety and performance).

For the lung cancer screening indication, the document refers to "clinical literature, including the results of the National Lung Screening Trial (N Engl J Med 2011; 365:395-409) and subsequent literature." This means the ground truth for the clinical utility of low-dose CT screening, in general, is based on outcomes data from large clinical trials. The device's "acceptance" for this indication is based on its capability to perform scans at low dose within established protocols, implying it meets the technical requirements to participate in such screening programs.

8. The sample size for the training set:

Not applicable. This is not for a machine learning model that requires a "training set" of clinical data in the typical sense.

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

Not applicable. No machine learning training set is described.

In summary: This 510(k) document is a regulatory submission for a general-purpose CT scanner. Its "acceptance criteria" and "proof" primarily revolve around demonstrating that its technical specifications, design, and functionality are substantially equivalent to a previously cleared predicate device, and that any modifications (like extended scan range, increased slices, or removal of gantry tilt) do not compromise safety or effectiveness. The inclusion of "low dose CT lung cancer screening" as an indication relies on the device's ability to perform scans compatible with established clinical guidelines, rather than presenting novel AI performance data for nodule detection or characterization.

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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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Pinnacle3® Radiation Therapy Planning System is a software package intended to provide planning support for the treatment of disease processes. Pinnacle3® Radiation Therapy Planning System incorporates a number of fully integrated subsystems, including Pinnacle3 Proton, which supports proton therapy planning. The full Pinnacle3® Radiation Therapy Planning System software package provides planning support for the treatment of disease processes, utilizing photon, proton, electron and brachytherapy techniques.

Pinnacle3® Radiation Therapy Planning System assists the clinician in formulating a treatment plan that maximizes the dose to the treatment volume while minimizing the dose to the surrounding normal tissues. The system is capable of operating in both the forward planning and inverse planning modes. Plans generated using this system is used in the determination of the course of a patient's radiation treatment. They are to be evaluated, modified and implemented by qualified medical personnel.

The Pinnacle3® Radiation Therapy Planning System (hereafter Pinnacle3® RTP System) provides radiation treatment planning for the treatment of benign or malignant diseases. When using the Pinnacle3® RTP System, qualified medical personnel may generate, review, verify, approve, print and export the radiation therapy plan prior to patient treatment. The Pinnacle3® RTP System can provide plans for various radiation therapy modalities including utilizing photon, proton, electron and brachytherapy techniques.

The Pinnacle3® RTP System is a software package that runs on an Oracle Server and is accessed through one (or more) client(s) or an Oracle UNIX workstation. The software package consists of a core software module (Pinnacle3) and optional software features, which are available through a licensing scheme. The device has network capability to other Pinnacle® RTP System workstations, thin client, and to both input and output devices via local area network (LAN) or wide area network (WAN).

Image data is imported from CT, MR, PET, PET-CT and SPECT devices using a DICOM-compliant interface. A qualified medical professional uses the Pinnacle3® RTP System for functions such as viewing and analyzing the patient's anatomy, and generating a radiation therapy plan.

The provide text is a 510(k) premarket notification for the Philips Medical Systems (Cleveland), Inc. Pinnacle3® Radiation Therapy Planning System. This document focuses on demonstrating substantial equivalence to a predicate device and does not contain detailed information about the acceptance criteria and study proving device performance as typically understood for AI/ML devices.

Here's an analysis based on the information provided, highlighting what is present and what is not present:

Key Takeaways from the Document:

• Device Type: This is a Radiation Therapy Planning System, a software package. It's not explicitly an AI/ML device in the sense of making diagnostic predictions or interpretations, although some features (like Auto-segmentation, Deformable Image Registration) could potentially leverage AI/ML techniques. The document describes it as "software" and emphasizes "physics modeling," "dose computation," and "optimization processes."
• Focus on Substantial Equivalence: The primary goal of this 510(k) is to demonstrate that the updated Pinnacle3® RTP System (Version 16.0) is substantially equivalent to its predicate device (K130992). This pathway typically relies on comparing new features to existing, approved functionalities rather than extensive novel clinical effectiveness studies.
• Verification Testing, Not Clinical Trials: The document explicitly states: "Clinical trials were not performed as part of the development of this product. Clinical testing on patients is not advantageous in demonstrating substantial equivalence or safety and effectiveness of the device since testing can be performed such that no human subjects are exposed to risk. Verification testing was performed as required per the risk analyses and demonstrated that no new risks were introduced with the modifications in this submission." This strongly indicates that the "study" proving the device meets acceptance criteria involved non-clinical verification testing rather than a traditional performance study with a test set, ground truth, and human experts.
• Acceptance Criteria Mentioned but Details Lacking: The document states that "The results of these tests demonstrate that the Pinnacle3® RTP System met the acceptance criteria and is adequate for its intended use." However, what those specific acceptance criteria were (e.g., specific quantitative thresholds for dose calculation accuracy, registration accuracy, etc.) and the detailed results are not described in this summary.

Given the document's nature as a 510(k) summary focused on substantial equivalence and non-clinical verification, many of the requested items (especially those related to clinical performance studies, expert consensus, sample sizes for test/training sets, and AI-specific metrics) are not present or applicable in the provided text.

Here's how to fill out your requested table and information, with many entries indicating "Not Applicable" or "Not Provided" based on the document:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not provided. The verification testing likely used various defined test cases or phantoms rather than a "test set" of patient data in the typical sense of a clinical performance study for an AI device.
• Data Provenance: Not applicable/not provided for patient data. The development and verification likely used simulated or phantom data, or possibly a limited set of de-identified patient data for specific test cases.

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

• Number of Experts: Not provided.
• Qualifications of Experts: Not provided.
• Note: Since this was primarily verification testing of a planning system rather than a diagnostic AI device, the "ground truth" would likely be established by engineering specifications, known physical properties (e.g., for dose calculations in phantoms), or clinical standards, rather than direct expert annotation of medical images for a test set.

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

• Adjudication Method: Not applicable/none. The document describes "verification testing" against specified requirements and risk analyses, not a human reader study requiring adjudication.

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 Study: No. The document explicitly states "Clinical trials were not performed." This type of study is typically done for AI-assisted diagnostic devices.
• Effect Size: Not applicable.

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

• Standalone Performance: While the system calculates and optimizes plans, the performance study described is non-clinical verification testing of the software's adherence to specifications and risk mitigation. The device is intended to "assist the clinician," and plans "are to be evaluated, modified and implemented by qualified medical personnel," indicating it's not a standalone diagnostic device but a tool for medical professionals. The verification testing itself would be "standalone" in the sense that the software's outputs were checked against expected results as part of the engineering validation.

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

• Type of Ground Truth: Not explicitly stated but inferred from "verification testing" and "risk analyses." Likely involved:
  • Engineering Specifications: Outputs (e.g., calculated dose, image registration accuracy) compared against predefined numerical tolerances.
  • Physics Models: Accuracy of dose calculations verified against established radiation physics models and experimental data from phantoms.
  • Clinical Standards: Verification that the system generates plans that align with accepted clinical practices and safety parameters for radiation therapy.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable/not provided. This is a software planning system with physics models and optimization algorithms, not a machine learning model that relies on a "training set" in the conventional sense for image classification or prediction tasks. While some features like "Atlas Auto-Segmentation" or "Model-Based Segmentation" might involve pre-trained models, the document does not provide details on their training data.

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

• How Ground Truth was Established: Not applicable/not provided for a training set in the AI/ML context. If certain sub-features (e.g., auto-segmentation) use models, the method for establishing their "ground truth" for training is not disclosed in this document.

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The Ingenuity CT is 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, patient and equipment supports, components and accessories. The Ingenuity CT is indicated for head, whole body, cardiac and vascular X-ray Computed Tomography applications in patients of all ages.

These scanners are intended to be used for diagnostic imaging and 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 Philips Ingenuity CT consists of three system configurations, the Philips Ingenuity CT, the Philips Ingenuity Core and the Philips Ingenuity Core128. These systems are Computed Tomography X-Ray Systems intended to produce cross-sectional images of the body by computer reconstruction of X-ray transmission data taken at different angles and planes. These devices may include signal analysis and display equipment, patient, and equipment supports, components and accessories. These scanners are intended to be used for diagnostic imaging and for low dose CT lung cancer screening for the early detection of Jung nodules that may represent cancer*.

The main components (detection system, the reconstruction algorithm, and the x-ray system) that are used in the Philips Ingenuity CT have the same fundamental design characteristics and are based on comparable technologies as the predicate.

The main system modules and functionalities are:

• 1. Gantry. The Gantry consists of 4 main internal units:
  • a. Stator a fixed mechanical frame that carries HW and SW.
  • b. Rotor A rotating circular stiff frame that is mounted in and supported by the stator.
  • c. X-Ray Tube (XRT) and Generator fixed to the Rotor frame.
  • d. Data Measurement System (DMS) a detectors array, fixed to the Rotor frame.
• 2. Patient Support (Couch) carries the patient in and out through the Gantry bore synchronized with the scan.
• 3. Console A two part subsystem containing a Host computer and display that is the primary user interface and the Common Image Reconstruction System (CIRS) - a dedicated powerful image reconstruction computer.

In addition to the above components and the software operating them, each system includes a workstation hardware and software for data acquisition, 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.

Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided text:

Important Note: The provided document is a 510(k) submission for a CT scanner (Philips Ingenuity CT), which focuses on demonstrating substantial equivalence to a predicate device (Philips Plus CT Scanner), rather than establishing new performance claims with specific acceptance criteria and clinical trial results typical for entirely novel AI/ML devices. Therefore, much of the requested information, particularly regarding AI-specific performance (like effect size of human reader improvement with AI, standalone AI performance, ground truth for training AI models) is not directly present. The clinical evaluation described is a comparative image quality assessment rather than a diagnostic accuracy clinical trial.

1. Table of Acceptance Criteria and Reported Device Performance

Given the nature of this 510(k) submission, the "acceptance criteria" are primarily established against international and FDA-recognized consensus standards for medical electrical equipment and CT systems, and against the performance of the predicate device. The "reported device performance" refers to the successful verification against these standards and equivalence to the predicate.

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

The document does not specify a distinct "test set" sample size in the sense of a number of clinical cases or patient images used for a diagnostic accuracy study. Instead, it refers to:

• Bench tests: These involved phantom images and physical testing of the system (e.g., patient support/gantry positioning repeatability and accuracy, laser alignment accuracy, CT image quality metrics testing). No sample size for these is given.
• Clinical Evaluation: An "image evaluation" was performed involving "images of the brain, chest, abdomen and pelvis/peripheral orthopedic body areas." The number of images or patient cases used for this evaluation is not specified.
• Data Provenance: Not explicitly stated, but given it's a Philips product, it's likely internal development and validation data. There is no mention of external datasets or specific countries of origin. The evaluation compares FBP and iDose4 reconstructions of the same images. The clinical evaluation implicitly relates to retrospective data as it compares reconstructed images.

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

• Number of Experts: "a qualified radiologist". So, one expert.
• Qualifications of Experts: Described only as "a qualified radiologist." No specific experience (e.g., years of experience, subspecialty) is provided.

4. Adjudication Method for the Test Set

The evaluation was performed by a single radiologist using a 5-point Likert scale. Therefore, no adjudication method (like 2+1, 3+1 consensus) was used as there was only one reviewer.

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

No, an MRMC comparative effectiveness study was not done. The document describes an image evaluation by a single radiologist, not multiple readers. It also describes a comparison of image quality between reconstruction techniques (FBP vs. iDose4), not a comparison of human reader diagnostic performance with vs. without AI assistance.

• Effect size of human readers improving with AI vs without AI assistance: This information is not applicable as this type of study was not performed. The study evaluated if iDose4-reconstructed images (which is an iterative reconstruction technique for image quality improvement and dose reduction, not an AI for diagnosis) maintained diagnostic quality compared to standard FBP.

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

Yes, in spirit, the primary evaluation is about the algorithm's output quality. The iDose4 iterative reconstruction algorithm directly produces images without human intervention, and these images were then evaluated by a radiologist. The core of this 510(k) is about the technical performance and safety of the CT scanner and its components, including its reconstruction algorithms. The evaluation described ("image evaluation...") is a standalone assessment of the image quality produced by the iDose4 algorithm compared to standard FBP. It is not an "AI diagnostic algorithm" standalone performance, but rather an "image reconstruction algorithm" standalone performance.

7. The Type of Ground Truth Used

For the clinical image evaluation, the "ground truth" was established by the evaluation of a qualified radiologist using a 5-point Likert scale to determine if images were of "diagnostic quality" and for comparing image quality between reconstruction methods. This could be considered a form of "expert consensus," albeit from a single expert in this case. There is no mention of pathology or outcomes data being used as ground truth for this specific image quality assessment.

8. The Sample Size for the Training Set

Not applicable in the context of this 510(k) as presented.

The device (Philips Ingenuity CT) is a hardware CT scanner with associated software, including image reconstruction algorithms (like iDose4). While iterative reconstruction algorithms might involve some form of "training" or optimization during their development, the document does not speak to a "training set" in the sense of a dataset used to train a machine learning model for a specific diagnostic task that would typically be described in an AI/ML device submission. The description focuses on technical modifications and adherence to engineering and safety standards, and performance against a predicate device.

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

Not applicable for the reasons stated above (no "training set" for an AI/ML diagnostic model described).

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The Philips Multislice CT Systems are Computed Tomography X-Ray Systems intended to produce cross-sectional images of the 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. The scanners are intended to be used for diagnostic imaging and 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 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.

Philips Low Dose CT Lung Cancer Screening option can be used with Philips whole body multi-slice CT X-Ray Systems installed in a healthcare facility (clinic / hospital). These systems provide a continuously rotating X-ray tube and detector array with 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. Reconstruction algorithms available are standard reconstruction (filtered back projection), iDose 4 and IMR iterative reconstruction. These systems also include signal analysis and display equipment, patient and equipment supports, components and accessories.

There are no functional, performance, feature, or design changes required for the qualified CT systems onto which the LDCT LCS Option is applied. Because none of the CTs will require hardware or software modifications, the Philips Low Dose CT Lung Cancer Screening option and the currently marketed and predicate Philips Multislice CT System for qualified CT systems in the installed base consists of:

• A set of up to three reference LDCT LCS protocols: standard reconstruction, standard reconstruction with iDose 4, and with IMR iterative reconstruction (where applicable), for each qualified CT System on a per CT platform basis;
• Detailed instructions on how to create the protocols on the corresponding CT System; and
• A dedicated Instructions for Use for LDCT LCS that covers all qualified systems.

This document is a 510(k) premarket notification for the Philips Multislice CT System with Low Dose CT Lung Cancer Screening. It primarily focuses on demonstrating substantial equivalence to existing predicate devices, rather than presenting a standalone study proving the device meats specific acceptance criteria in a clinical setting with human readers.

However, based on the provided text, I can infer the acceptance criteria relate to maintaining image quality parameters for Low Dose CT Lung Cancer Screening (LDCT LCS) that are equivalent to or better than standard CT performance, especially given the reduced radiation dose. The "study" proving this largely relies on non-clinical bench testing and references to external clinical literature and trials.

Here's a breakdown of the information you requested:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly state "acceptance criteria" in a quantitative, measurable sense for a clinical study with patients. Instead, it focuses on demonstrating that the LDCT LCS option does not degrade image quality compared to existing CT systems and that existing clinical evidence supports the efficacy of LDCT LCS itself.

Here's a table based on the image quality parameters evaluated in non-clinical testing and the reported outcome of that testing:

2. Sample size 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 applicable in the context of a clinical patient test set for this specific submission. The "test set" for demonstrating substantial equivalence related to image quality was composed of various CT scanner models and reconstruction algorithms.
• Data Provenance: The primary data used to support the efficacy of LDCT LCS itself comes from externally referenced clinical literature, specifically the National Lung Screening Trial (NLST) (N Engl J Med 2011; 365:395-409) and the International Early Lung Cancer Action Program (I-ELCAP), along with "subsequent literature." These were large-scale prospective clinical trials, likely conducted across multiple centers, including within the US (NLST).

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

• Number of Experts: Not applicable for this specific submission's non-clinical testing. The non-clinical image quality phantom measurements do not involve expert interpretation for ground truth.
• Qualifications of Experts: For the external clinical trials (NLST, I-ELCAP) referenced, radiologists and other medical professionals were involved in establishing diagnoses and outcomes, but their specific numbers and qualifications are not detailed in this 510(k) document.

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

• Adjudication Method: Not applicable for this specific submission's non-clinical testing. No human adjudication was performed for the image quality metrics tested.

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: No, a multi-reader, multi-case comparative effectiveness study with human readers (with vs. without AI assistance) was not conducted or presented in this 510(k) submission. This submission is for the CT system itself with a low-dose protocol option, not for an AI-powered CAD (Computer-Aided Detection) or CADx (Computer-Aided Diagnosis) device. The device does not incorporate AI for interpretation.

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

• Standalone Performance Study: No, a standalone algorithm-only performance study was not conducted or presented in this 510(k) submission. The device is a CT scanner, not an independent algorithm for diagnosis or detection.

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

• Type of Ground Truth:
  • For the image quality bench testing: Ground truth was established by the physical phantoms used for measurements (e.g., wire phantom for MTF, ramp/disk phantom for slice thickness, water/density phantoms for CT numbers).
  • For the clinical efficacy of LDCT LCS (referenced externally): The ground truth for the NLST and I-ELCAP studies would have included pathology reports (for confirmed cancers), clinical follow-up/outcomes data (for stable or resolving nodules), and potentially expert consensus reviews of indeterminate findings.

8. The sample size for the training set

• Training Set Sample Size: Not applicable. This submission describes a CT scanner with a new protocol, not a machine learning algorithm that requires a training set. The reconstruction algorithms (iDose4, IMR) themselves would have been developed using various data, but details are not provided here.

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

• Ground Truth for Training Set: Not applicable, as this is not an AI/ML device requiring a training set with ground truth established through labeled data in the context of this submission.

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A nuclear medicine image display and processing application suite that provides software applications used to process, analyze and display medical images/data. The results obtained may be used as a tool, by a nuclear physician, in determining the diagnosis of patient disease conditions in various organs, tissues, and other anatomical structure. The data processed may be derived from any nuclear medicine gamma camera. JETPack 2.0 should only be operated by qualified healthcare professionals trained in the use of nuclear medicine equipment.

JETPack 2.0 is a Windows®-based Nuclear Medicine suite of image display and processing applications for the Nuclear Medicine market segment. The software package is deployable on hardware platforms, which meet the minimum requirements needed to run the software. The comprehensive tools and features provided with this product, will allow the technologist and/or physician to perform image review, processing of source data, post processing, hardcopy production, interpretation, report . generation and contains the utilities necessary to support the workflow and data management between those activities.

This 510(k) summary does not contain the information requested for acceptance criteria or specific study details to prove the device meets acceptance criteria.

The document is a premarket notification for a new version of a Picture Archiving and Communication System (PACS) software, JETPack 2.0. It primarily focuses on establishing substantial equivalence to a predicate device (Philips Medical Systems NM Application Suite, K080961).

Here's why the requested information is not present:

• Acceptance Criteria and Reported Device Performance: This document states that JETPack 2.0 and the predicate device "perform in a similar manner with respect to, display, review and processing applications." It concludes that JETPack 2.0 is "substantially equivalent based on similar intended use, technological comparison, and system performance." However, it does not define specific acceptance criteria (e.g., quantitative metrics, thresholds) or present a table of reported device performance against such criteria. The "system performance" is described in a general, rather than a quantifiable, manner.
• Sample Size for Test Set and Data Provenance: This information is not provided. No specific test set or clinical study data is referenced.
• Number of Experts and Qualifications: This information is not provided. There's no mention of experts establishing ground truth for a test set.
• Adjudication Method: Not applicable, as no test set requiring adjudication is mentioned.
• Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study: No MRMC study is mentioned. The document relies on substantial equivalence to a previous device rather than a new comparative effectiveness study.
• Standalone Performance: While the description focuses on the software's functionality, there's no standalone performance study in the context of diagnostic accuracy or clinical impact presented here.
• Type of Ground Truth Used: Not applicable, as no specific ground truth for a performance study is described.
• Sample Size for Training Set: This information is not provided. The development process of the software, including any training data for AI/ML components (if present, which is not explicitly stated for this 2011 submission), is not detailed.
• How Ground Truth for Training Set Was Established: Not applicable, as no training set or its ground truth establishment is described.

In summary, this 510(k) filing demonstrates substantial equivalence by comparing the device's intended use and general technological characteristics to a legally marketed predicate device. It does not present detailed performance studies with specific acceptance criteria, test sets, or ground truth establishment that would be required for innovative devices making new claims or for devices undergoing more rigorous evaluation pathways.

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