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The CELLTRACKS® AUTOPREP® System is a laboratory instrument used with immunomagnetic reagents that capture and enrich target cells, and labeling reagents that differentiate cells in whole blood. The CELLTRACKS ANALYZER II® 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® may be used for cell identification and enumeration following processing. The CELLTRACKS® 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.

This is a 510(k) summary for the CELLTRACKS® AUTOPREP® System, a medical device for in vitro diagnostic use. The purpose of this submission is to demonstrate substantial equivalence to a predicate device (CELLTRACKS® AUTOPREP® System (current - K110406)). The only change is a modification to the pipetting probe to reduce potential carryover and a corresponding label change.

Here's a breakdown of the requested information based on the provided text:

1. A table of acceptance criteria and the reported device performance

The provided 510(k) summary does not explicitly state numerical acceptance criteria for carryover. Instead, it focuses on demonstrating equivalence to the predicate device and characterization of the new probe's performance regarding carryover.

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

The document does not explicitly state the sample size used for the test set in terms of the number of patient samples. It mentions "up to 8 samples in a batch" for processing but this refers to the operational capacity of the instrument, not the sample size of the study for performance validation.

• Test Set Sample Size: Not explicitly stated for each test, but "up to 8 samples in a batch" is mentioned for processing.
• Data Provenance: Not explicitly stated (e.g., country of origin, retrospective/prospective). The testing is described as "Non-clinical functional testing," implying laboratory-based testing rather than patient data collection in a clinical setting.

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

Not applicable. This device is an automated instrument for processing and enriching cells. The evaluation appears to be based on analytical performance metrics (e.g., carryover levels, assay performance equivalence) rather than expert interpretation of images or patient data. Therefore, there's no mention of experts establishing ground truth for the test set in this context.

4. Adjudication method for the test set

Not applicable, as there are no human interpretations or classifications that would require adjudication. The testing is focused on the device's functional performance.

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 is not an AI-assisted diagnostic device that involves human readers. It's an automated sample preparation system.

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

This refers to a standalone product. The CELLTRACKS® AUTOPREP® System is a standalone instrument for sample preparation. The performance evaluation described is of the instrument's functional characteristics (e.g., pipetting, carryover), which is inherently "standalone" in its operation relative to the manual methods it automates. The "algorithm" in this context would be the automated processing steps of the instrument.

7. The type of ground truth used

The "ground truth" for this device would be defined by the expected analytical performance. For example:

• Known Cell Spikes: For CTC spike level characterization, a known number of tumor cells would be "spiked" into samples, and the device's ability to recover them, and the level of carryover from these known spikes, would be measured.
• Control Cells: For control cell carryover, known control cells would be used.
• Reference Methods: The "new reagent probe versus current reagent probe assay performance equivalence" would imply comparison to assays run with the predicate device's probe, where the "truth" is the established performance of the predicate.
• Engineering Specifications: Reliability/Life testing would be against pre-defined engineering specifications for durability and performance over time.

Therefore, the ground truth is based on analytical standards, known input concentrations (e.g., spiked cells), and comparison to a well-characterized predicate device's performance.

8. The sample size for the training set

Not applicable. This device is not an AI/ML algorithm that requires a "training set." It's an automated instrument where the "learning" is incorporated during its design and engineering phases, not through a data-driven training process.

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

Not applicable, as there is no training set for this type of device.

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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®, 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.

This document is a FDA 510(k) clearance letter and does not contain the detailed information required to answer the prompt. Specifically, it does not describe the acceptance criteria, the study details, sample sizes, expert qualifications, or ground truth establishment. It merely states that the device, CellTracks® AutoPrep® System, is substantially equivalent to a legally marketed predicate device for its indicated use.

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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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