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510(k) Data Aggregation

    K Number
    K052920
    Device Name
    DELTA4
    Manufacturer
    Date Cleared
    2006-01-12

    (87 days)

    Product Code
    Regulation Number
    892.5050
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The intended use of Delta4 is quality assurance of patient specific treatment delivery prior to the treatment in IMRT and 4DRT (respiratory gating and tumor tracking).

    Device Description

    The new device consists out of:

    • software .
    • . phantom
    • Detector arrays. .
    • Multi-channel electrometer .
    • Connection cables .

    When measurements are to be performed the device is typically put on the patient table (or couch). Then the device is exposed typically from different angles.

    AI/ML Overview

    Here's an analysis of the provided text regarding the acceptance criteria and study for the Delta4 device:

    Based on the provided document, the device described is the Delta4, a quality assurance system for patient-specific treatment delivery in IMRT (Intensity Modulated Radiation Therapy) and 4DRT (4-Dimensional Radiation Therapy, including respiratory gating and tumor tracking).


    1. Table of Acceptance Criteria and Reported Device Performance

    The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to predicate devices rather than establishing novel acceptance criteria for a new clinical claim. Therefore, explicit, quantified acceptance criteria and performance metrics in the typical sense (e.g., sensitivity, specificity, accuracy against a gold standard) are not detailed in this document.

    Instead, the document asserts:

    • The new device "is partly better, partly equivalent and in some cases identical with the predicate devices regarding safety, design and performance." (Section 9 CONCLUSION)
    • It improves workflow: The customer can automatically perform quality assurance in one single session, avoiding two exposures (IMRT vs. 4DRT measurement instruments).
    • Technological advantages are listed for its semiconductor detectors over ionization chambers and films/TLD (smaller volume, no external high voltage, high-resolution/online readout).

    Summary Table (based on document's claims, not quantitative metrics):

    Acceptance Criteria CategoryReported Device Performance (based on claims)
    SafetyEquivalent to predicate devices; designed for IEC 601-1 and IEC 601-1-2 conformance; inherently safe due to absence of high voltage in semiconductor technology.
    DesignEquivalent/identical with predicate devices.
    PerformanceEquivalent/partly better than predicate devices, especially regarding workflow for IMRT and 4DRT in single session. Semiconductor detectors offer advantages like smaller volume, no external HV, faster readout.
    Intended UseAchieves quality assurance of patient-specific treatment delivery prior to IMRT and 4DRT.

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

    The document does not specify a discrete "test set" sample size or data provenance in the context of a clinical performance study. The 510(k) summary focuses on design characteristics and technological equivalence rather than presenting results from a clinical trial with a defined patient cohort.

    The statement "Clinical tests are not necessary; however, the device has been tested in a clinical environment" (Section 8) implies some form of evaluation but provides no details on:

    • The number of cases or studies involved.
    • The country of origin for any data generated.
    • Whether any data was retrospective or prospective.

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

    This information is not provided in the document. As clinical tests with explicit ground truth establishment were deemed "not necessary" for this 510(k), no details on experts or their qualifications for establishing ground truth are present. The "typical user" is identified as a "Physicist or other dosimetry expert," suggesting these professionals would evaluate the device's output in a real-world setting.


    4. Adjudication Method for the Test Set

    Since no specific "test set" and ground truth establishment process are described, an adjudication method is not mentioned.


    5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size of AI Improvement

    No MRMC comparative effectiveness study is mentioned. The device is a quality assurance system for radiation therapy, an "algorithm only" type of device for measurement and comparison, not an AI-assisted diagnostic or treatment planning tool that would typically involve human readers.


    6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

    The device is inherently a standalone (algorithm only) performance system in terms of its core function. It measures dose distribution using detectors and its software compares this measured dose to a calculated dose plan from an external Treatment Planning System (TPS). The "human-in-the-loop" aspects involve the physicist interpreting the comparison results and making decisions based on them.

    The comparison of calculated and measured dose is performed "inside the device's software" (Section 4.2, point 6), indicating its standalone algorithmic function.


    7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

    The document describes the device's function as comparing a measured dose distribution to a calculated dose distribution (from a TPS). In this context, the "ground truth" for the device's comparison is the calculated dose distribution from the Treatment Planning System. The device's purpose is to verify if the actual delivered dose (measured) matches the planned dose (calculated).


    8. The Sample Size for the Training Set

    The document does not mention a "training set" as would be typical for machine learning-based algorithms. The device's technology appears to be based on established physics principles of semiconductor detectors and dose measurement/comparison, not on a machine learning model that requires training data.


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

    As no training set is mentioned or implied, the question of how its ground truth was established is not applicable to this document.

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