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510(k) Data Aggregation

    K Number
    K963451
    Device Name
    ANTI-TUMOR EXPERT SYSTEM (ATES) FOR RADIATION THERAPY TREATMENT PLANNING
    Manufacturer
    TOPSLANE
    Date Cleared
    1997-08-05

    (336 days)

    Product Code
    IYE
    Regulation Number
    892.5050
    Type
    Traditional
    Panel
    Radiology
    Reference & Predicate Devices
    K940237
    Why did this record match?
    Reference Devices :

    K940237

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The ARTP (Anti-Tumor Radiation Treatment Planning System) is a radiation dose planning and simulation of patients undergoing external beam treatment in the oncology clinic. ARTP is used to plan and simulate radiation treatments with linear accelerators and other similar teletherapy devices with x-ray energies from 1 to 25MV, as well as Cobalt-60, and electron energies from 1 to 25 MeV.

    Device Description

    The TOPSLANE ATES is a Radiotherapy Treatment Planning System (RTPS) for radiation dose planning of patients undergoing external beam treatment in the oncology clinic. ATES is using modem photon beam convolution dose calculation algorithm and electron pencil beam dose calculation algorithm. The system software is designed to be convenient and low cost to the user.

    AI/ML Overview

    The provided text describes a Radiation Treatment Planning System (RTPS) called TOPSLANE ATES, which is a medical device subject to FDA 510(k) review. The acceptance criteria and supporting studies for such devices typically focus on demonstrating substantial equivalence to a previously cleared predicate device, rather than explicit numerical performance targets like sensitivity or specificity.

    Here's an analysis based on the provided text:

    1. Table of Acceptance Criteria and Reported Device Performance:

    Acceptance Criteria (Implicit for 510(k))Reported Device Performance
    Intended Use Equivalence: Same indications for use as predicate.The intended use is the same as the predicate device (Theraplan V05B). The ATES is used for radiation dose planning of patients undergoing external beam treatment in oncology clinics, with linear accelerators, cobalt-60, and electron energies from 1 to 25 MeV.
    Technological Characteristics Equivalence: No significant differences in design, materials, energy source, etc. compared to predicate.No significant change in design, materials, energy source, or other technological characteristics compared to the predicate device. Minor configuration differences do not alter intended use or affect safety/effectiveness.
    Safety and Effectiveness: Does not raise new questions of safety and effectiveness.Performance tests indicated that the system consistently performed within the design parameters and equivalently to the predicate device. The device was designed and manufactured to meet IEC 601-1, IEC 601-1.1, and IEC 878 standards.
    Compliance with Standards/Guidance: Adherence to relevant medical device standards and FDA guidance documents.Complies with IEC 601-1, IEC 601-1.1, IEC 878 standards, and the FDA, CDRH, ODE, August 29, 1991, Reviewer Guidance for Computer Controlled Medical Devices Undergoing 510(k) Review.

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

    The document does not specify a separate "test set" in the context of clinical data or patient outcomes. The performance evaluation appears to be based on:

    • Engineering/System Performance Tests: "Performance tests were conducted and the results indicated that the system consistently performed within the design parameters and equivalently to the predicate device."
    • Comparison to Predicate: The core of a 510(k) is demonstrating equivalence. This often involves comparing technical specifications, functionalities, and calculation results of the new device against the predicate.

    Therefore, there's no explicitly mentioned sample size for a test set comprising patient data, nor is there information on data provenance (e.g., country of origin, retrospective/prospective). The "test set" here refers to the device itself and its internal performance metrics.

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

    Not applicable in this context. The "ground truth" for a RTPS in a 510(k) submission generally involves established physics principles, dose calculation algorithms, and comparisons to recognized "gold standard" methods or the predicate device's output, rather than expert consensus on patient images or diagnoses.

    4. Adjudication Method for the Test Set:

    Not applicable. There's no mention of an adjudication process for a test set in the clinical sense.

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

    No, an MRMC study was not done. The document does not describe human readers or AI assistance in the context of improving diagnostic accuracy. This device is a treatment planning system, not an AI-assisted diagnostic tool.

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

    The performance evaluation described seems to be a standalone assessment of the system's dose calculation and planning capabilities. The statement "Performance tests were conducted and the results indicated that the system consistently performed within the design parameters and equivalently to the predicate device" implies testing the algorithm and its output directly, without a human-in-the-loop component being the primary focus of the equivalence demonstration.

    7. The Type of Ground Truth Used:

    The "ground truth" for demonstrating equivalence would likely be:

    • Predicated Device Performance/Output: The results obtained from the predicate device (Theraplan V05B) for specific treatment planning scenarios.
    • Physics Principles/Established Models: The accuracy of the "modern photon beam convolution dose calculation algorithm and electron pencil beam dose calculation algorithm" would be evaluated against established physics models for radiation dose distribution.
    • Engineering Specifications: The device's output meeting its own design parameters for dose calculation, planning geometry, etc.

    8. The Sample Size for the Training Set:

    Not applicable. This device is a rule-based/algorithmic system for radiation treatment planning, not a machine learning or AI system that requires a separate "training set" in the conventional sense. Its "training" is embodied in its programmed algorithms and adherence to established physics.

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

    Not applicable, as there is no "training set" for this type of device mentioned in the document.

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