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510(k) Data Aggregation

    K Number
    K202134
    Date Cleared
    2021-04-08

    (251 days)

    Product Code
    Regulation Number
    876.5130
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    Well Lead All Silicone Foley Catheter with Temperature Sensor

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

    All Silicone Foley Catheter With Temperature Sensor is intended for use in the drainage/collection of urine from the urinary bladder and simultaneous monitoring of the body core temperature during surgical intervals.

    Device Description

    The All Silicone Foley Catheter with Temperature Sensor is made from medical grade silicone, consists of temperature sensor for monitoring core body temperature and Foley catheter including a shaft, drainage funnel, inflation funnel, balloon, valve, and X-ray opaque line. The device is provided sterile by ethylene oxide and is for single use only. It is provided in a variety of sizes and color-coded by size.

    AI/ML Overview

    The provided text is a 510(k) summary for a medical device called "All Silicone Foley Catheter with Temperature Sensor." It discusses the device's intended use, description, and comparison to a predicate device, focusing on its MR compatibility. However, the document does not contain information regarding acceptance criteria and a study proving that the device meets these criteria in the context of AI/ML performance.

    The document is a regulatory submission for a physical medical device (catheter) and the "testing" mentioned refers to bench-top non-clinical testing for MR compatibility of the physical device, not performance benchmarks for an AI/ML algorithm.

    Therefore, I cannot extract the requested information. The document does not describe:

    1. A table of acceptance criteria and reported device performance (for an AI/ML system).
    2. Sample size used for an AI/ML test set or data provenance.
    3. Number of experts used to establish ground truth or their qualifications.
    4. Adjudication method for a test set.
    5. Multi-reader multi-case (MRMC) comparative effectiveness study.
    6. Standalone (algorithm-only) performance.
    7. Type of ground truth used for AI/ML.
    8. Sample size for a training set.
    9. How ground truth for a training set was established.
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