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The CellTracks® AutoPrep® System is a general purpose laboratory instrument used with immunomagnetic reagents that capture and enrich target cells, and labeling reagents that differentiate cells in whole blood. Cell analyzers such as the CellTracks Analyzer II®, CellSpotter® System, flow cytometers or microscopes may be used for cell identification and enumeration. The system is for in vitro diagnostic use.

The CellTracks® AutoPrep® System is a general purpose laboratory instrument used with immunomagnetic reagents that capture and enrich target cells, and labeling reagents that differentiate cells in whole blood. The CellTracks® AutoPrep® System processes up to 8 samples in a batch, performing all required process steps, including red cell detection, plasma aspiration and final transfer to the analysis cartridge. The user is prompted to perform various pre-processing operations such as dilution and centrifugation. Cell analyzers such as the CellTracks Analyzer II®, CellSpotter® System, flow cytometers or microscopes may be used for cell identification and enumeration following processing.

The AutoPrep® system uses a series of immunomagnetic separation procedures to isolate the cells of interest and to stain the cells with fluorescence-labeled monoclonal antibodies.

The provided text describes a 510(k) premarket notification for the CellTracks® AutoPrep® System. This is a general purpose laboratory instrument for automated blood cell preparation, not an AI/ML powered diagnostic device. Therefore, much of the requested information (such as AI performance metrics, expert adjudication, MRMC studies, and training/test set details) is not applicable or cannot be extracted from this document.

However, I can extract the acceptance criteria related to its substantial equivalence to the predicate device and the study that demonstrates this.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

The document does not specify a distinct "test set" in the context of AI/ML. The evaluation was based on "functional testing of the bulk fluid module" and "performance testing using quality control samples." The specifics of these samples (e.g., number, type, origin) are not detailed.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. Ground truth as typically defined for AI/ML validation (e.g., expert consensus on images or pathology) is not relevant for this device's evaluation as it is a laboratory instrument, not an interpretive diagnostic.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable. This concept is typically associated with expert review of diagnostic outputs, which is not described for this device.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not applicable. This device is a pre-analytic instrument, not an AI-powered diagnostic that assists human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

This refers to the performance of the instrument itself. The study mentioned "functional testing of the bulk fluid module" and "performance testing using quality control samples," which represents the standalone performance of the modified device.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The "ground truth" for this device would be the expected functional output and performance metrics based on established laboratory standards and comparisons to the predicate device. This is indicated by "functional testing" and "performance testing using quality control samples." The specific methodology for establishing these performance benchmarks is not detailed beyond these general terms.

8. The sample size for the training set

Not applicable. This device is an instrument, not an AI/ML model that requires a training set.

9. How the ground truth for the training set was established

Not applicable.

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