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510(k) Data Aggregation

    K Number
    K244023
    Manufacturer
    Date Cleared
    2025-01-24

    (28 days)

    Product Code
    Regulation Number
    876.1520
    Reference & Predicate Devices
    Why did this record match?
    Reference Devices :

    K223473

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

    ME-APDS (Magentig Eye's Automatic Polyp Detection System) is intended to be used by endoscopists as an adjunct to the common video colonoscopy procedure (screening and surveillance), aiming to assist the endoscopist in identifying lesions during colonoscopy procedure by highlighting reqions with visual characteristics consistent with different types of mucosal abnormalities that appear in the colonoscopy video during the procedure. Highlighted regions can be independently assessed by the endoscopist and appropriate action taken according to standard clinical practice.

    ME-APDS is trained to process video images which may contain regions consistent with polyps.

    ME-APDS is limited for use with standard white-light endoscopy imaging only.

    ME-APDS is intended to be used as an adjunct to endoscopy procedures and is not intended to replace histopathological sampling as means of diagnosis.

    Device Description

    ME-APDS™MAGENTIQ-COLO is intended to be used as an adjunct to the common video colonoscopy procedure. The system application aims to assist the endoscopist in identifying lesions, such as polyps, during the colonoscopy procedures in real time. The device is not intended to be used for diagnosis or characterization of lesions, and does not replace clinical decision making.

    The system acquires the digital video output signal from the local endoscopy camera and processes the video frames. It runs deep machine learning and additional supporting algorithms in real time on the video frames in order to detect and identify regions having characteristics consistent with different types of mucosal abnormalities such as polyps. The output video with the detected lesions is presented on a separate screen, highlighting the suspicious areas on the original video. The user can also take snapshots of the videos, with and without the highlighting of the suspicious areas, record videos and view in full screen mode.

    AI/ML Overview

    Here's an analysis of the acceptance criteria and study details for the MAGENTIQ-COLO device, based on the provided document:

    1. Acceptance Criteria and Reported Device Performance

    The acceptance criteria are implied by the reported performance metrics, particularly "Polyp-wise Recall" and "False Positives Per Frame (FPPF)". The study aims to demonstrate that the device performs comparably to or better than the predicate device.

    Acceptance Criteria / MetricReported Device Performance (Full Testing Dataset)
    Polyp-wise Recall (PRecall1)97.9% [96.63%, 98.94%]
    Polyp-wise Recall (PRecall3)95.3% [93.39%, 96.96%]
    Polyp-wise Recall (PRecall5)93.2% [91.01%, 95.15%]
    Polyp-wise Recall (PRecall7)90.6% [88.19%, 92.91%]
    False Positives Per Frame (FPPF)0.0333 (threshold achieved)
    Polyps with Histology: PRecall199.7% [99.12%, 100.0%]
    Polyps with Histology: PRecall799.7% [99.11%, 100.0%]
    Median Coverage of Polyps (with histology)81.7%
    Marker Annotation Latency (Median)133 msec for FHD, 157 msec for 4K

    Note: The document states that "The testing results were observed to be as expected and support that the device has similar performance to the predicate device," implying that these reported values met the implicit acceptance criteria for substantial equivalence.

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

    • Sample Size (Test Set): 212 unique full colonoscopy videos, containing 702 polyps (16 videos contained no polyps).
    • Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective.

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

    The document does not explicitly state the number of experts used to establish the ground truth or their specific qualifications (e.g., "radiologist with 10 years of experience"). However, it references polyps "verified by histology" and "reported in the procedure report," implying clinical expert input.

    4. Adjudication Method for the Test Set

    The document does not describe a specific adjudication method like 2+1 or 3+1. The ground truth seems to be derived from documented polyps in the "procedure report" and "histology findings," suggesting a standard clinical reporting process rather than a specific consensus method for this study.

    5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

    No, a multi-reader multi-case (MRMC) comparative effectiveness study was not reported in this document. The study described is a standalone performance test of the algorithm. The document mentions that the clinical validation used to support the device's polyp detection functions was conducted in a previous submission (K223473). This K223473 submission might contain an MRMC study, but it's not detailed here.

    6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

    Yes, a standalone performance study was done. The "Standalone Performance Testing" section describes how "The algorithm was tested offline" on an independent dataset to evaluate its recall, false positive performance, and false positives per full video rate without direct human interaction during the test.

    7. Type of Ground Truth Used

    The ground truth used for the test set was a combination of:

    • Histopathology findings: For polyps with histology reports.
    • Procedure reports: For polyps identified and documented during the colonoscopy procedure.

    8. Sample Size for the Training Set

    The document does not provide the sample size for the training set. It only states that "ME-APDS is trained to process video images which may contain regions consistent with polyps."

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

    The document does not provide information on how the ground truth for the training set was established. It only broadly states that the system "runs deep machine learning" and is "trained to process video images."

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