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510(k) Data Aggregation

    K Number
    K121074
    Device Name
    SCENIUM 2.0
    Date Cleared
    2012-06-08

    (60 days)

    Product Code
    Regulation Number
    892.2050
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    SCENIUM 2.0

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

    The Scenium display and analysis software has been developed to aid the Clinician in the assessment and quantification of pathologies taken from PET and SPECT scans.

    The software is deployed via medical imaging workplaces and is organized as a series of workflows which are specific to use with particular drug and disease combinations.

    The software aids in the assessment of human brain scans enabling automated analysis through quantification of mean pixel values located within standard regions of interest. It facilitates comparison with existing scans derived from FDG-PET, amyloid-PET, and SPECT studies and calculation of uptake ratios between regions of interest.

    Device Description

    Scenium 2.0 display and analysis software enables visualization and appropriate rendering of multimodality data, providing a number of features which enable the user to process the acquired image data.

    Scenium 2.0 is post processing and does not control the scanning features of the system.

    AI/ML Overview

    The provided text is a 510(k) Premarket Notification for Scenium 2.0. This document focuses on establishing substantial equivalence to previously cleared devices rather than presenting a detailed study with acceptance criteria and performance data.

    Therefore, many of the requested details about acceptance criteria, study design, and performance metrics are not available within this document. The 510(k) submission primarily relies on comparing the new device's technological characteristics, indications for use, and safety/effectiveness considerations to those of predicate devices.

    Here's an analysis based on the available information:

    1. A table of acceptance criteria and the reported device performance

    Not available in the provided text. The document does not specify quantitative acceptance criteria or report specific performance metrics for Scenium 2.0. It primarily asserts that "All requirements of Emission Computed Tomography system standards (21 CFR 892.1200) and Picture Archiving and Communications System (21 CFR 892.2050) are met, and software is in compliance with ISO 14971 and ISO 62304." This is a statement of compliance with standards rather than specific performance data against acceptance criteria.

    2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

    Not available in the provided text. No information on a specific test set, its size, or data provenance is mentioned.

    3. 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 available in the provided text. This information would typically be detailed in a separate clinical study report, which is not part of this 510(k) summary.

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

    Not available in the provided text.

    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 available in the provided text. The document describes Scenium 2.0 as "post processing" software that "aids the Clinician" and "enables automated analysis," suggesting it is a human-in-the-loop device. However, no MRMC study or its findings (effect size of improvement with AI assistance) are mentioned.

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

    Not explicitly stated. Given the description of the software aiding clinicians and facilitating automated analysis, it's likely intended for human-in-the-loop use. Standalone performance, if assessed, is not reported in this document.

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

    Not available in the provided text.

    8. The sample size for the training set

    Not available in the provided text. The document does not describe any machine learning model training.

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

    Not available in the provided text.

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