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The XN-Series modules (XN-10, XN-20) are quantitative multi-parameter automated hematology analyzers intended for in vitro diagnostic use in screening patient populations found in clinical laboratories.

The XN-Series modules classify and enumerate the following parameters in whole blood: WBC, RBC, HGB, HCT, MCV, MCH, MCHC, PLT (PLT-I, PLT-F), NEUT%/#, LYMPH%/#, MONO%/#, EO%/#, BASO%/#, IG%/#, RDW-CV, RDW-SD, MPV, NRBC#/%, RET%/#, IPF, IRF, RET-He and has a Body Fluid mode for body fluids. The Body Fluid mode enumerates the WBC-BF, RBC-BF, MN%/#, PMN%/#, and TC-BF parameters in cerebrospinal fluid (CSF), serous fluids (peritoneal, pleural) and synovial fluids. Whole blood should be collected in K₂ or K₃EDTA anticoagulant and, Serous and Synovial fluids in K₂EDTA anticoagulant to prevent clotting of fluid. The use of anticoagulants with CSF specimens is neither required nor recommended.

The Sysmex® XN-Series modules (XN-10, XN-20) are multi-parameter hematology analyzers intended to perform tests on whole blood samples collected in K₂ or K₃EDTA and body fluids (pleural, peritoneal and synovial) collected in K₂ anticoagulant. It can also perform tests on CSF which should not be collected in any anticoagulant. The instrument consists of four principal units: (1) Two Main Units (XN-10, XN-20) which aspirate, dilute, mix, and analyze blood and body fluid samples; (2) Two Auto Sampler Units (SA-10, SA-20) which supply samples to the Main Unit automatically; (3) IPU (Information Processing Unit) which processes data from the Main Unit and provides the operator interface with the system; (4) Pneumatic Unit which supplies pressure and vacuum from the Main Unit. The XN-Series analyzers perform analysis using the following methods: RF/DC Detection Method, Sheath Flow DC Detection Method, and Flow Cytometry Methods using a Semiconductor Laser. Particle characterization and identification is based on detection of forward scatter, fluorescence and adaptive cluster analysis. The XN-Series analyzers automatically classify cells from whole blood and body fluids and carry out all processes automatically from aspiration of the sample to outputting the results.

The body fluid analysis mode of the XN-Series analyzers uses the 4DIFF scattergram & the RBC distribution obtained from a specialized analysis sequence to calculate & display the WBC (WBC-BF) counts, mononuclear cell (MN) / polymorphonuclear cell (PMN) counts & percentages, TC-BF (Total Count) & RBC (RBC-BF) counts found in the body fluid.

Analysis results and graphics are displayed on the IPU screen. They can be printed on any of the available printers or transmitted to a Host computer.

Here's a breakdown of the acceptance criteria and study information for the Sysmex XN-Series Automated Hematology Analyzers, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document primarily focuses on establishing substantial equivalence to a predicate device (Sysmex XE-5000) rather than explicitly stating acceptance criteria values for each parameter. Instead, it states that "Studies were performed to evaluate the equivalency of the XN-Series Automated Hematology analyzers (Modules XN-10, XN-20) to the XE-5000 Automated Hematology analyzer. Results indicated equivalent performance."

However, it does describe the types of tests performed to demonstrate performance:

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

The document does not explicitly state the sample size used for the test set. It mentions "Studies were performed," but no specific numbers of samples are provided.

Regarding data provenance, the document does not specify the country of origin of the data or whether it was retrospective or prospective.

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

This information is not provided in the given text. The document refers to "manufacturer's specifications" for performance but does not detail how ground truth for the clinical performance data was established or if it involved human expert review for individual cases.

4. Adjudication Method for the Test Set

This information is not provided in the given text.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly conducted or described in the provided text. The study focused on demonstrating "equivalency" to a predicate device rather than comparing human reader performance with and without AI assistance from the device.

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

Yes, a standalone performance study was conducted. The document states: "Data consisting of Accuracy, Precision, Linearity and Carryover were collected to show performance to the manufacturer's specification for the Body Fluid mode." This implies a technical validation of the instrument's (algorithm's) ability to measure parameters directly, independent of human interpretation of the results beyond standard quality control.

7. The Type of Ground Truth Used

The type of ground truth used is implicit in the nature of hematology analyzers:

• For parameters like WBC, RBC, HGB, etc., the ground truth would typically be established by reference methods or calibrated controls that are considered highly accurate.
• For the body fluid mode, the "manufacturer's specifications" would define the expected accurate values against which the device's measurements (Accuracy, Precision, Linearity, Carryover) are compared. These specifications are likely derived from internal validation studies using reference methods.

8. The Sample Size for the Training Set

The document does not provide information regarding a training set. As this is an automated hematology analyzer performing quantitative measurements based on established physical and chemical principles (RF/DC Detection, Sheath Flow DC Detection, Flow Cytometry), traditional machine learning "training sets" are not typically a primary focus for regulatory submissions of this type. The device's "training" would involve the design and calibration of its internal algorithms and measuring channels.

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

Since a traditional "training set" for a machine learning model is not explicitly mentioned as relevant in this context, the method for establishing its ground truth is not applicable/provided. The device's operational principles are based on known physics and established clinical measurement techniques.

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