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510(k) Data Aggregation

    K Number
    K061559
    Date Cleared
    2006-08-11

    (67 days)

    Product Code
    Regulation Number
    862.1785
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    ACON URINALYSIS REAGENT STRIPS

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

    The ACON Urinalysis Reagent Strips (Urine) are for qualitative and semi-quantitative detection of one or more of the following analytes in urine: Glucose, Bilirubin, Ketone (Acetoacetic acid), Specific Gravity, Blood, pH, Protein, Urobilinogen, Nitrite, Leukocytes and Ascorbic Acid.

    The ACON Urinalysis Reagent Strips (Urine) are for single use in professional nearpatient (point-of-care) and centralized laboratory locations. The strips are intended for use in screening at-risk patients to assist diagnosis in the following areas:

    • Kidney function .
    • Urinary track infections .
    • Carbohydrate metabolism (e.g. diabetes mellitus) .
    • Liver function .
    • Acid-base balance .
    • Urine concentration ●

    The results can be used along with other diagnostic information to rule out certain disease states and to determine if microscopic analysis is needed. The test is to be read visually. It is intended for professional use only.

    Device Description

    The ACON Urinalysis Reagent Strips are urine test strips of which Glucose, Bilirubin, Ketone, Specific Gravity, Blood, pH, Protein, Urobilinogen, Nitrite and Leukocyte reagent pads are affixed onto the plastic strips. The reagent pads react with the urine and provide a visible color reaction. Results are obtained by direct comparison of the test strip with the color blocks printed on the bottle label. The product is packaged with a drying agent in a plastic bottle. The entire reagent strip is disposable when the disposal directions are followed exactly. Laboratory instrumentation is not required. These tests are intended for professional use with human urine.

    AI/ML Overview

    The provided text describes the ACON Urinalysis Reagent Strips, their intended use, and a comparison to predicate devices, but it does not contain specific acceptance criteria, detailed study results, or information about sample sizes for training/test sets, ground truth establishment, expert qualifications, or MRMC studies.

    Therefore, for aspects related to specific numerical acceptance criteria, detailed performance data, sample sizes, expert involvement, and comparative effectiveness studies, the information is not available in the provided document.

    Here's an analysis of what information is available:


    1. Table of Acceptance Criteria and Reported Device Performance

    Not available in the provided document. The document states that "performance characteristics... were verified by sensitivity study, reproducibility study, interference studies, temperature flex study, and stability studies," but it does not specify the quantitative acceptance criteria for these studies or present the detailed reported performance values against such criteria.

    The submission focuses on establishing substantial equivalence based on intended use, target population, specimen type, materials, storage, test time, and methodologies used, which are listed as "Same" as the predicate devices.

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

    Not available in the provided document. The document mentions "clinical studies were conducted at Beta sites" but does not specify the sample size for these studies or the country of origin. It also does not explicitly state whether the data was retrospective or prospective.

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

    Not available in the provided document. The document states the "test is to be read visually" and "intended for professional use only," implying human interpretation. However, it does not detail how ground truth was established for the clinical studies, nor if experts were involved, their number, or their qualifications.

    4. Adjudication Method for the Test Set

    Not available in the provided document. There is no mention of any adjudication method (e.g., 2+1, 3+1) used for the test set.

    5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

    No. A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not performed or described. The document discusses "clinical accuracy of results" and "comparable testing data" between the ACON strips and predicate devices, which suggests an equivalence study rather than a comparative effectiveness study involving human readers with and without AI assistance (which is not relevant for this type of manual test).

    6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Study Was Done

    Partially applicable, but not in the context of an algorithm. The device is a manual urinalysis reagent strip intended for visual reading by a professional. Therefore, the concept of an "algorithm only" or "standalone" performance study in the context of AI is not applicable. The device is the standalone test, and its performance is inherently tied to human interpretation of the visual reaction. The document does state that the test is "to be read visually" and "intended for professional use only."

    7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

    Not explicitly stated for clinical studies. While the document describes the chemical reactions for each analyte (e.g., glucose, bilirubin, ketone, etc.), forming the basis of the test, it does not specify how the "ground truth" was established for the clinical studies used to compare the ACON strips to the predicate devices. For urinalysis, ground truth often involves laboratory reference methods, but this is not detailed here.

    8. The Sample Size for the Training Set

    Not applicable. The ACON Urinalysis Reagent Strips are a chemical test, not an AI/machine learning algorithm. Therefore, there is no "training set" in the computational sense. The product development would involve chemical optimization and validation, not model training.

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

    Not applicable. As stated above, this is not an AI/machine learning device, so the concept of a training set and its ground truth establishment is not relevant.

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