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The AutoloGel Process Centrifuge is designed to be used at the patient's point of care for the safe and rapid preparation of platelet-rich plasma (PRP) from a small sample of the patient's blood (up to 60 ml).

The AutoloGel Process Centrifuge consists of a table-top, non self-decanting, rotor spin centrifuge and processing disposables designed to allow for rapid automatic separation of plasma and red blood cells from a small volume of whole blood. The centrifuge spins at a maximum speed of 7200 rpms producing a maximum force of approximately 4227 g.

This is a 510(k) summary for a medical device called the "AutoloGel Process Centrifuge." This document focuses on demonstrating substantial equivalence to previously cleared devices rather than establishing new acceptance criteria or conducting extensive performance studies like those typically seen for AI/ML devices. Therefore, many of the requested fields are not applicable or cannot be extracted from this type of submission.

Here's a breakdown of the information based on the provided text, with explanations where fields are not applicable:

1. Table of Acceptance Criteria and Reported Device Performance

Formal acceptance criteria, as one might see for performance metrics (e.g., sensitivity, specificity for an AI diagnostic), are not presented in this 510(k). The submission focuses on comparing the technological characteristics of the AutoloGel Process Centrifuge to predicate devices. The "performance" reported is inherent in its design specifications and its ability to achieve its intended use, which is to rapidly separate platelet-rich plasma (PRP).

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

Not applicable. This 510(k) is for a general-purpose centrifuge, not an AI/ML device that would typically have a "test set" in the context of performance evaluation. The submission refers to performance data as "not provided" for this section (presumably because it's a mechanical device where performance is demonstrated through engineering specifications and comparison to predicates, not through clinical trials or retrospective data analysis of diagnostic outputs).

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

Not applicable. As this is not an AI/ML diagnostic or predictive device, there is no "ground truth" established by experts in the context of image interpretation or diagnostic accuracy.

4. Adjudication Method for the Test Set

Not applicable. No test set requiring adjudication is described.

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 device for human readers.

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

Not applicable. This is not an algorithmic device.

7. The Type of Ground Truth Used

Not applicable. The "ground truth" for a centrifuge relates to its physical performance (e.g., achieving specified RPMs, RCFs, separation efficiency). This is demonstrated through engineering testing and design specifications, not through a "ground truth" methodology as used in AI/ML performance studies.

8. The Sample Size for the Training Set

Not applicable. This device is not developed using machine learning, so there is no training set.

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

Not applicable. As there is no training set.

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