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510(k) Data Aggregation

    K Number
    K201005

    Validate with FDA (Live)

    Device Name
    FullFocus
    Manufacturer
    Date Cleared
    2020-07-15

    (90 days)

    Product Code
    Regulation Number
    864.3700
    Age Range
    All
    Reference & Predicate Devices
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticPediatricDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    FullFocus™ is a software only device intended for viewing and management of digital images of scanned surgical pathology slides prepared from formalin-fixed paraffin embedded (FFPE) tissue. It is an aid to the pathologist to review, interpret, and manage digital images of pathology slides for primary diagnosis. FullFocus is not intended for use with frozen sections, cytology, or non-FFPE hematopathology specimens.

    It is the responsibility of a qualified pathologist to employ appropriate procedures and safeguards of the images obtained and, where necessary, use conventional light microscopy review when making a diagnostic decision. FullFocus is intended for use with Philips Ultra Fast Scanner and monitor displays validated with verified test methods to meet required performance characteristics.

    Device Description

    FullFocus is a web-based software-only device for viewing and manipulating digital pathology images of glass slides obtained from the Philips IntelliSite Pathology Solution (PIPS) Ultra Fast Scanner (UFS) on the monitor displays that are validated with verified test methods to meet required performance characteristics. FullFocus reproduces the whole slide images and is an aid to the pathologist to review. interpret and manage digital images of pathology slides for primary diagnosis.

    AI/ML Overview

    Here's a breakdown of the acceptance criteria and the study information for the FullFocus device, based on the provided FDA 510(k) summary:

    Acceptance Criteria and Reported Device Performance

    Acceptance CriteriaReported Device Performance
    Pixel-wise comparisonVisually adequately reproduces digital pathology images to human readers with respect to its intended use (compared to PIPS, including zooming and panning).
    Turnaround time (Case selection)Not longer than 10 seconds until the image is fully loaded.
    Turnaround time (Panning)Not longer than 7 seconds until the image is fully loaded (for panning one quarter of the monitor).
    Measurements AccuracyPerforms accurate measurements (verified using a test image containing objects with known sizes).
    Human factors testingFound to be safe and effective for the intended users, uses, and use environments; user interface is intuitive, safe, and effective for the range of intended users.

    Further Study Information

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

      • Clinical Study: No clinical study involving diagnosis by human readers for diagnostic accuracy comparison is mentioned in this document. The "studies" described are non-clinical technical performance assessments and human factors testing.
      • Pixel-wise comparison: The document doesn't specify a sample size for slides or images, only that it "was conducted to compare color images reproduced by FullFocus and PIPS IMS." Data provenance is not mentioned, but it's implied the images were generated by a Philips Ultra Fast Scanner, given the device's compatibility and comparison to the PIPS IMS.
      • Measurements: "a test image containing objects with known sizes" was used. Specific sample size is not indicated.
      • Human Factors Testing: "Task-based usability tests" were performed. The number of participants (intended users) is not specified.
    2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

      • For the pixel-wise comparison, the "ground truth" was essentially the visual fidelity to images produced by the predicate device (PIPS IMS). The "human readers" mentioned in the performance description are not described as experts establishing ground truth, but rather as observers confirming visual adequacy. No specific number or qualifications of these readers are given.
      • For measurements, the ground truth was the "known sizes" of objects within a test image. This would not require expert pathologists to establish.
      • For human factors testing, the "ground truth" relates to usability and safety, which is assessed directly by intended users during task performance, rather than established by an "expert" in the diagnostic sense.
    3. Adjudication method for the test set:

      • Not applicable as there was no study described that involved diagnostic interpretations requiring adjudication.
    4. If a multi reader multi case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance:

      • No. The document explicitly states that FullFocus is a "software only device intended for viewing and management of digital images... It is an aid to the pathologist to review, interpret, and manage digital images...". It is a viewer, not an AI-assisted diagnostic tool. Therefore, an MRMC study comparing human readers with and without AI assistance was not performed or described.
    5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

      • No. FullFocus is a viewing and management system for pathologists, not a standalone diagnostic algorithm. Its function is to facilitate human review.
    6. The type of ground truth used:

      • For Pixel-wise comparison, the ground truth was the visual representation and fidelity of images from the predicate device (PIPS IMS).
      • For Measurements, the ground truth was "known sizes" of objects in a test image.
      • For Turnaround time and Human factors testing, the ground truth was based on pre-defined system requirements and direct usability observations/feedback.
    7. The sample size for the training set:

      • Not applicable. FullFocus is described as a viewing and management software, not an AI or machine learning algorithm that requires a training set in the typical sense for diagnostic performance.
    8. How the ground truth for the training set was established:

      • Not applicable, as no training set for an AI/ML algorithm is described.
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