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The intended use for the Immunicon CellTracks™ AutoPrep System is as a generalpurpose 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, 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 an automated sample-handling instrument that starts with a tube of anticoagulated whole blood and delivers an enriched, processed sample that is ready to analyze by flow-cytometry, fluorescent microscopy, CellSpotter™ System or by the CellTracks™ Analyzer. The AutoPrep System performs several steps, including red cell detection, plasma aspiration and filling of a sample chamber or test tube. The principal of operation relates to the addition of a ferrofluid, which has been conjugated with monoclonal antibodies that act with the system to magnetically separate the cells of interest and in subsequent steps, within the system, to add fluorescencelabeled monoclonals to further differentiate the captured cells. The first reagent added is ferrofluid, which consists of a magnetic core surrounded by a protein layer coated with antibodies for attachment to cells. Ferrofluid particles are colloidal and when mixed with a sample containing the target cells, they interact and attach to the target cells. The ferrofluid/sample mixture is placed in a strong magnetic field, which causes the labeled target cells to move to the side of the tube. The blood is aspirated, the magnetic field is removed and the cells are resuspended in a small volume of buffer and fluorescent reagents are added for the identification and enumeration of the target cells. Another magnetic separation step and resuspension is performed and the sample is now ready for analysis. The immunomagnetic enrichment process is the new technology but does not raise any new issues of safety and effectiveness.

The provided document is a 510(k) summary for the Immunicon CellTracks™ AutoPrep System. It does not contain information about acceptance criteria or a study proving that a device meets specific performance criteria in the context of an AI/ML medical device, as the prompt's structure implies. This device is a sample-handling instrument for in vitro diagnostic use, not an AI-based system.

However, I can extract the relevant performance data that was collected as part of the 510(k submission.

Here's an analysis based on the information provided, framed to best fit the request, while acknowledging the limitations of the document's content for an AI/ML context:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" in a tabular format with predefined numerical targets. Instead, it describes performance characteristics observed during clinical and non-clinical testing. I will present the reported performance as if these were the metrics assessed.

The document discusses two main types of studies: a precision study and a method comparison study.

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

• Test Set Sample Size: The document states a "20 day precision study" was performed and a "method comparison was performed." It does not explicitly state the number of samples or patients used in these studies. The precision study implies repeated measurements over 20 days.
• Data Provenance: Clinical testing was performed at "three clinical sites." The document does not specify the country of origin, but given the FDA submission, it's highly likely to be within the United States. The studies are prospective in nature, as they involve performing tests with the device to evaluate its performance.

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

The document does not mention the use of experts to establish a "ground truth" for the test set in the context of diagnostic interpretation. The studies are technical performance evaluations of an automated sample preparation system. The "ground truth" for the precision study would be the known spike levels of control cells, and for the method comparison, it would be the results obtained by the predicate device. Therefore, no external experts were explicitly mentioned for ground truth establishment.

4. Adjudication Method for the Test Set

Not applicable. This is not a study requiring adjudication of diagnostic interpretations. The studies described are analytical performance evaluations.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No. This is not an AI/ML diagnostic device; it's an automated sample preparation system. Therefore, an MRMC study comparing human readers with and without AI assistance is not relevant to this submission and was not conducted.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

Yes, in the sense that the performance evaluations (precision, sensitivity, linearity, method comparison) assess the device's technical capabilities as a standalone automated system. There's no "human-in-the-loop" component for the sample preparation itself, although subsequent analysis of the processed samples would involve human operators and/or other analytical instruments.

7. Type of Ground Truth Used

• Precision Study: The ground truth for the precision study was based on "control cells" at "spike levels" (38 cells and 264 cells). This implies a known, controlled input to assess reproducibility.
• Nonclinical Testing (Sensitivity, Linearity): The ground truth for sensitivity and linearity was derived from known cell counts introduced into the system to determine its ability to detect and accurately quantify them.
• Method Comparison: The ground truth for the method comparison was the results obtained from the "predicate device." This establishes a comparative baseline against an already legally marketed device for the same intended use.

8. Sample Size for the Training Set

Not applicable. This is not an AI/ML device that requires a training set. The device operates based on pre-programmed protocols and immunomagnetic separation principles, not on learned patterns from a training dataset.

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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