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510(k) Data Aggregation

    K Number
    K050589
    Date Cleared
    2005-05-04

    (57 days)

    Product Code
    Regulation Number
    864.5220
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    RET-HE PARAMETER ON THE SYSMEX MODEL XE-2100 AUTOMATED HEMATOLOGY ANALYZER

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

    The RET-He parameter on the Sysmex® XE-2100, Automated Hematology Analyzer, determines the hemoglobin of reticulocytes for in vitro diagnostic use in clinical laboratories.

    Device Description

    The XE-2100 is an automated hematology analyzer previously cleared by the FDA. The RET-He parameter determines the hemoglobin of the reticulocytes. (Note: XE-Pro and RET Master are required to obtain results described.)

    AI/ML Overview

    Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided text:

    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance CriterionReported Device Performance
    Accuracy (comparison to predicate method)Comparison to CHr showed excellent correlation.
    Equivalent performance to predicate methodStudies were performed to evaluate the equivalency of the RET-He parameter to the predicate method. Results indicated equivalent performance.
    Substantial EquivalenceThe performance data demonstrated substantial equivalence.

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

    The provided text does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective or prospective). It only mentions that "Studies were performed to evaluate the equivalency of the RET-He parameter to the predicate method."

    3. Number of Experts Used to Establish Ground Truth and Their Qualifications

    The provided text does not mention using experts to establish ground truth or their qualifications. The study focused on comparing the new device's output (RET-He) to a predicate device's output (CHr).

    4. Adjudication Method for the Test Set

    The provided text does not describe an adjudication method. Since the study compared a device's output to a predicate device's output, and not to a human-established ground truth, adjudication would likely not be relevant in the same way it is for image interpretation tasks.

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

    No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This type of study is more common for diagnostic devices where human interpretation plays a significant role and AI aims to augment that interpretation. This submission concerns a hematology analyzer, where the device performs the measurement without human-in-the-loop interpretation.

    6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

    Yes, the study described is effectively a standalone (algorithm only without human-in-the-loop performance) study. The RET-He parameter is determined by the Sysmex XE-2100 analyzer using its internal methodology, and its performance was evaluated by comparing its results directly to those of the predicate device (Bayer Advia 120). There is no indication of human intervention in interpreting the results from either device for the purpose of this comparison.

    7. The Type of Ground Truth Used

    The ground truth used in this study was the output of the predicate device (CHr parameter on the Bayer Advia 120 Hematology System). The study aimed to demonstrate "excellent correlation" and "equivalent performance" of the new RET-He parameter to this established predicate method.

    8. The Sample Size for the Training Set

    The provided text does not specify a sample size for any training set. This type of device (hematology analyzer) likely has its algorithms and parameters established during its initial development and validation, rather than through a separate "training set" in the context of machine learning. The submission focuses on the performance comparison of the developed device.

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

    Since no specific "training set" is mentioned in the context of this 510(k) submission, the question of how ground truth for it was established is not applicable based on the provided text. The device's methodology for deriving reticulocyte parameters is described ("using the reticulocyte forward scattered light signals from the reticulocyte measurement channel and a proprietary Sysmex calculation equation"), implying a deterministic process rather than a machine learning model trained on a ground-truthed dataset.

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