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To be utilized by a trained physician through a flexible endoscope for the collection of duodenal or jejunal fluids for diagnostic analysis.

The Hobbs Medical Aspiration Catheter is a sheath within a sheath design. The inner sheath is actuated using a proximal handle. The inner sheath is protected by an elastomeric membrane that has a slit at the distal end to allow for the inner sheath to advance. The inner sheath has three side holes at the distal tip for collection of aspirate. Once the sample is collected the inner sheath is retracted to protect the sample from contamination as it is withdrawn from the endoscope.

The provided text describes a 510(k) premarket notification for an Aspiration Catheter, which is a medical device for collecting duodenal or jejunal fluids. However, the document does not contain information about a study involving an AI/Machine Learning component, nor does it provide the detailed specifics required to answer your questions about acceptance criteria, study design, ground truth, or expert involvement in the context of an AI-based device.

The document primarily focuses on demonstrating the substantial equivalence of the new Aspiration Catheter to a legally marketed predicate device (Hobbs Medical/Jayco Aspiration Catheter K844550) through physical performance testing, rather than a clinical study evaluating an AI algorithm's performance.

Therefore, I cannot provide the requested information based on the provided text, as it does not pertain to the development or validation of an AI/ML medical device.

Here's a breakdown of why I cannot answer each question based on the provided text:

1. A table of acceptance criteria and the reported device performance: The document mentions "Joint integrity testing," "Deployment force testing," "Endoscope compatibility testing," "Design verification and validation," "Protection of sample from contamination," and "Shelf life testing." However, specific numerical acceptance criteria (e.g., "deployment force must be less than X N") and the quantitative results for these tests are not provided. These are engineering/device performance tests, not AI performance metrics.
2. Sample sized used for the test set and the data provenance: No information on sample sizes for clinical or AI-specific testing is available. The provenance of any data (country of origin, retrospective/prospective) is also not mentioned, as it's not a data-driven AI study.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. This document does not describe a study involving expert annotation or ground truth establishment for an AI algorithm.
4. Adjudication method: Not applicable. There's no AI algorithm requiring adjudication.
5. MRMC comparative effectiveness study: Not applicable. This is not an AI-assisted human reader study.
6. Standalone (algorithm only without human-in-the loop performance) study: Not applicable. This device is not an algorithm.
7. The type of ground truth used: Not applicable. The "ground truth" for this device would relate to its physical performance meeting specifications, not clinical diagnoses or classifications.
8. The sample size for the training set: Not applicable. No AI training was conducted.
9. How the ground truth for the training set was established: Not applicable. No AI training was conducted.

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