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510(k) Data Aggregation

    K Number
    K223313

    Validate with FDA (Live)

    Date Cleared
    2023-01-23

    (87 days)

    Product Code
    Regulation Number
    878.4850
    Reference & Predicate Devices
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The product is used to obtain capillary blood samples from fingertip in a home. The device contains a sharp injury protection feature.

    Device Description

    The Disposable Safety Lancets consists 7 parts, include a trigger, plastic handle, out shell, back cover, spring, protective cap and needle. The models of the Disposable Safety Lancets are 21G; 23G; 26G; 28G; 30G. The product is used to obtain capillary blood samples from fingertip in a hospital or at home. The device contains a sharp injury feature. The lancet is hit by pressure, and once the device strikes, the lancet needle can puncture the skin. And once activated, the needle retracts into the body of the device which reduces the risk of injury as the result if an exposed needle. Used Gamma sterilization, and are products for single use.

    AI/ML Overview

    The provided text describes the 510(k) summary for a medical device (Disposable Safety Lancets) and focuses on demonstrating substantial equivalence to a predicate device, rather than the acceptance criteria and study data for an AI/algorithm-driven device.

    Therefore, most of the requested information regarding AI/algorithm performance (e.g., sample size for test/training sets, data provenance, number of experts for ground truth, adjudication methods, MRMC studies, standalone performance, etc.) cannot be extracted from this document, as it pertains to a physical medical device (blood lancet) and not an AI-powered diagnostic or assistive tool.

    However, I can extract the acceptance criteria and performance results for the physical device's non-clinical testing.

    Here's the information that can be extracted from the document:

    1. Table of Acceptance Criteria and the Reported Device Performance (Non-Clinical Testing for a Physical Device)

    NoTesting ItemAcceptance Criteria (Specification)Reported Device Performance (Result)
    01AppearanceDisposable Safety Lancets the surface should be smooth without edge, no dirt and damage, deformation and other poor appearance.Pass
    02Launch LengthThe length of the needles in the Disposable Safety Lancet is different in different gauges. The launch length of the needle is determined according to the length of the purchase, and the general emission length is 1.8mm-2.2mm.Pass
    03Sharpness/Penetration testingPenetration force ≤1.00N.Pass
    04FeatureThe tip of the needle can shrink quickly after firing, and the tip of the needle is not exposed.Pass
    05FeatureDisposable Safety Lancets Only one launch, not another.Pass
    06Initial bioburdenInitial bioburden of the device shall be less than 100CFU/gPass
    07SterileThe sterile blood lancet shall be sterilePass
    08Cap removal forceThe moment for breaking the safe mode should range from 30 Ncm to 35 Ncm.Pass
    09Needle removal forceThe bond between the lancet body and needle should be greater than or equal to 10N/15s.Pass
    10Drop testingThe carton box should have no puncture after the drop test.Pass

    Information that cannot be extracted from the provided text (as it's not relevant to this type of device submission):

    • Sample sizes used for the test set and data provenance (e.g., country of origin, retrospective/prospective).
    • Number of experts used to establish the ground truth for the test set and their qualifications.
    • Adjudication method for the test set.
    • If a multi-reader multi-case (MRMC) comparative effectiveness study was done, or the effect size of human readers improving with AI vs. without AI assistance.
    • If a standalone (algorithm only) performance study was done.
    • The type of ground truth used (expert consensus, pathology, outcomes data, etc.) for AI models.
    • The sample size for the training set (for AI models).
    • How the ground truth for the training set was established (for AI models).
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