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510(k) Data Aggregation

    K Number
    K151372
    Date Cleared
    2015-08-14

    (85 days)

    Product Code
    Regulation Number
    892.1750
    Reference & Predicate Devices
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Low Dose CT (LDCT) Lung Cancer Screening Option (LCS) for Qualified GE Systems is indicated for using low dose CT for lung cancer 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.

    Device Description

    There are not any functional, performance, feature, or design changes required for the Qualified CT system onto which the Option is applied.

    Because none of the CTs will require hardware or software modifications the subject device for qualified systems in the installed base consists of:

    1. a set of three reference LDCT LCS protocols (small, average, large patient) for each qualified CT System on a per CT platform basis;

    2. detailed instructions on how to transfer the protocols onto the corresponding CT System: and

    3. a dedicated for user's manual for LDCT LCS that covers all qualified systems.

    For qualified forward production systems, the three above elements that constitute the subject device for the qualified systems in the installed will be deployed in a modified manner:

    1. the 3 LDCT LCS reference protocols for the Qualified system will be loaded onto the system at the factory:

    2. because the reference LDCT LCS protocols will already be on the system, there will be no need for detailed instructions on how to manually enter the protocols; and

    3. the dedicated user manual for LDCT LCS may be folded in as a new separate chapter of the system's user manual.

    For qualified forward production systems, the LDCT LCS "device" will be structured into the qualified systems. This will result in all qualified forward production systems always incorporating this LDCT LCS Option.

    AI/ML Overview

    The provided document is a 510(k) Premarket Notification Submission for a "Low Dose CT Lung Cancer Screening Option for Qualified GE CT Systems". It describes the device's indications for use, technological characteristics, and arguments for substantial equivalence to predicate devices, rather than presenting a study specifically designed to establish acceptance criteria and prove the device meets those criteria with detailed performance metrics.

    However, from the document, we can infer some "acceptance criteria" through the image quality metrics used for evaluating substantial equivalence and the general claim that the device is "safe and effective". The study conducted to support this is a bench test using phantoms, rather than a clinical trial with human subjects.

    Here's an attempt to structure the information based on your request, with the understanding that not all specific details you asked for are explicitly provided in this type of regulatory document.


    1. Table of Acceptance Criteria and Reported Device Performance

    The acceptance criteria are implicitly derived from the ability of the qualified GE CT systems, using the new LDCT LCS protocols, to maintain image quality comparable to or better than predicate devices used in past clinical trials (like NLST) and to allow for the detection and sizing of lung nodules. The performance is assessed via phantom studies.

    Acceptance Criteria (Inferred)Reported Device Performance (from Phantom Study)
    CT number accuracy remains acceptable in low-dose conditions for calcified nodule reference.Not explicitly quantified, but general statement that image quality metrics were substantially equivalent to predicate devices.
    CT number uniformity is maintained throughout the lung for robust nodule detectability and structure separation in low-dose conditions.Not explicitly quantified, but general statement that image quality metrics were substantially equivalent to predicate devices.
    Image noise (standard deviation) allows for nodule detectability and sizing measurement.Acknowledged that noise increases with dose reduction, but the overall assessment implies it does not compromise detectability based on CNR and visual assessment.
    Modulation Transfer Function (MTF) preserves high contrast spatial resolution even at lower dose conditions for high-contrast nodules.General statement that image quality metrics were substantially equivalent to predicate devices.
    Visual Resolution/Image Artifacts (e.g., low signal streaks, beam hardening) do not obscure pathology or affect CT number accuracy for small object detection.General statement that image quality metrics were substantially equivalent to predicate devices.
    Noise Power Spectrum (NPS) does not significantly shift frequency or increase amplitude to compromise nodule detection.NPS measurements/analyses were performed; iterative reconstruction (IR) slightly shifted the NPS profile towards lower frequency without compromising nodule detection.
    Slice thickness maintains clear edges and boundaries of nodules and accurate nodule sizing.General statement that image quality metrics were substantially equivalent to predicate devices.
    Contrast to Noise Ratio (CNR) is sufficient to detect solid and non-solid nodules. (Primary figure of merit for nodule detectability)CNR measurements on 5mm solid and nonsolid nodules in a lung phantom were performed. Comparisons showed the level of conspicuity was maintained. Experienced imaging physicists and applications specialists easily saw the smallest, lowest contrast nodule in the lowest CNR images from representative systems. The bench test results showed "more than sufficient CNR for detecting and sizing of 5 mm or greater solid and nonsolid lung nodules".
    Accurate nodule sizing is maintained.Measurement results from the anthropomorphic chest phantom showed that "accurate sizing was also maintained" for 5mm or greater nodules.
    Device complies with US and international safety and performance standards.Stated compliance with 21 CFR Subchapter J, NEMA, DICOM, and IEC standards.

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

    • Test Set (Phantom Study): The test set primarily consisted of:
      • Standard IQ phantoms (e.g., Catphan for NPS).
      • An anthropomorphic clinical simulation lung phantom with 5mm solid and nonsolid nodules.
    • Sample Size:
      • For the IQ phantoms, it's not specified how many scans or instances were used, but rather the type of phantoms.
      • For the anthropomorphic lung phantom, specific nodules (5mm solid and nonsolid) were evaluated.
      • The "test set" also implicitly refers to the representative CT systems chosen: LightSpeed 16, Discovery CT590 RT, LightSpeed VCT and Optima CT 660, Discovery CT750 HD, and Revolution CT.
    • Data Provenance: The data was generated through bench testing (phantom studies) conducted by GE Medical Systems, LLC (GE Healthcare). The location of the testing is not explicitly stated but presumed to be internal GE facilities. This is a prospective generation of data for the 510(k) submission.

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

    • Number of Experts: "Experienced imaging physicists and applications specialists" were used. The exact number is not specified, but the plural "specialists" suggests more than one.
    • Qualifications: "Experienced imaging physicists and applications specialists." No specific number of years of experience or board certifications (like radiologist) are mentioned, as the evaluation was of phantom images, not clinical images.

    4. Adjudication Method for the Test Set

    • The document states, "In all cases, the small, lowest contrast nodule was easily seen" by the experienced imaging physicists and applications specialists. This suggests a consensus or affirmation rather than a formal adjudication method (like 2+1 or 3+1). Since it was a detectability assessment on phantom images rather than a diagnostic decision, a formal adjudication protocol appears to have been deemed unnecessary.

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

    • No, an MRMC comparative effectiveness study was not done for this specific 510(k) submission to demonstrate the effectiveness of the device itself.
    • The submission references large clinical trials like the National Lung Screening Trial (NLST) and I-ELCAP to establish the safety and effectiveness of LDCT Lung Cancer Screening in general, performed within established protocols, for which GE CT systems were previously used. However, these trials were not conducted to compare human readers with and without the specific GE device option at hand.
    • Effect size of human reader improvement: Not applicable, as no such study was performed.

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

    • Not applicable. The "Low Dose CT Lung Cancer Screening Option" is not an AI algorithm for nodule detection or diagnosis; rather, it is a set of optimized acquisition protocols and a user manual for existing GE CT systems to be used for LDCT lung cancer screening. Its performance is assessed by the physical image quality metrics it produces, which then aids human readers.

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

    • For the phantom study, the "ground truth" for the nodules (size, type, location) was inherent in the design of the anthropomorphic lung phantom. The detectability was then visually confirmed by experienced imaging physicists and applications specialists.
    • For the broader claim of the safety and effectiveness of LDCT LCS, the submission relies on the ground truth established by large-scale clinical trials (e.g., NLST) and medical professional society guidelines, which are based on clinical outcomes and expert consensus.

    8. The Sample Size for the Training Set

    • This device is not an AI/ML algorithm that requires a "training set" in the conventional sense. The "training" for developing the new LDCT LCS protocols involved:
      • Reviewing existing reference protocols for Chest CT on GE CT systems.
      • A literature review of current guidance on appropriate CT acquisition parameters, reconstructions, and system functional performance capabilities for LDCT LCS.
      • Synthesizing this information and guidance recommendations to determine acquisition and reconstruction attributes.
      • Using these attributes and GE CT system knowledge to develop the specific LDCT LCS Scan Parameters for each qualified CT system.
    • Therefore, there isn't a quantifiable "sample size" for a training set as would be found in an AI/ML device submission.

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

    • As noted above, there isn't a traditional "training set" with ground truth in the context of an algorithm. Instead, the protocol development was based on:
      • Existing product specifications and performance data: From GE CT systems.
      • Published clinical literature and guidelines: Reference publications, clinical trials (like NLST, I-ELCAP), medical professional society guides and recommendations (e.g., USPSTF, CMS decisions). These sources provided the "ground truth" (or accepted best practices) for what constitutes effective and safe LDCT LCS.
      • Expert knowledge: Internal GE CT system knowledge and the expertise of their engineers and applications specialists.
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