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The TriSalus Infusion System is intended for use in angiographic procedures to deliver radiopaque media and therapeutic agents to selected sites in the peripheral vascular system.

The TriSalus Infusion System is a 0.021" lumen coaxial microcatheter with the Expandable Tip at the distal end. The TriSalus Infusion System serves as the conduit for physician-specified agents. The device has a PTFE inner liner to provide a lubricious surface for passage of physician-specified agents and other accessory devices. The Expandable Tip and distal section (100 cm) of the microcatheter are hydrophilically coated. The soft, pliable, Expandable Tip is sized for use in vessels of 2 mm to 6 mm. There are four radiopaque markers located at the distal end of the TriSalus Infusion System to aid in positioning of Expandable Tip. The Expandable Tip can be expanded or collapsed up to 5 times for repositioning during an interventional procedure by moving the thumb-slide back and forth. When expanded, the Expandable Tip is designed to improve infusion efficiency of diagnostic and therapeutic agents. A commercially-available pressure transducer may be attached to the sideport luer connector if pressure measurement during infusion is desired. The system is provided sterile (EO) and for single patient use.

The provided text describes the TriSalus Infusion System and its substantial equivalence to a predicate device, focusing on its design, intended use, and performance testing. However, it does not contain the detailed information necessary to complete a comprehensive table of acceptance criteria and reported device performance for an AI/ML device, nor does it detail a study proving such criteria for AI.

This document is an FDA 510(k) clearance summary for a medical device (a microcatheter), not an AI/ML software. Therefore, the questions related to AI/ML specific criteria (e.g., sample size for test set, data provenance, ground truth establishment, MRMC studies, standalone performance, training set sample size, how ground truth was established for training) are not applicable to the information provided.

I can, however, extract related information from the document that might be relevant if this were about a traditional medical device test.

Based on the provided document, here is an analysis of the available information regarding acceptance criteria and performance, as much as it can be applied to a non-AI medical device.

1. Table of acceptance criteria and the reported device performance:

The document summarizes performance testing but does not provide specific quantitative acceptance criteria or detailed numerical performance results in a table format. It generally states that the device "meets its specified performance requirements" and "performed comparably to the predicate device."

Here's an attempt to structure a table based on the types of tests mentioned, but without specific numerical criteria or results:

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

• Sample Size for Test Set: Not specified in the document for any of the bench tests. For the animal study, the sample size is also not specified, only that "An animal study was performed."
• Data Provenance: Not specified.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

• This question is more applicable to AI/ML studies where human expert consensus often defines ground truth. For this medical device, performance requirements are likely based on engineering specifications and direct physical measurements.
• The animal study mentions "defined by physicians in a simulated clinical environment," implying expert input, but details on the number or qualifications of these physicians are not provided.

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

• Not applicable/Not specified for this type of medical device testing. Adjudication methods are typically used in clinical trials or AI/ML ground truth establishment.

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:

• No. This is an MRMC study for AI/ML. The document describes testing for a physical medical device (catheter), not an AI.

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

• Not applicable. This document is not about an algorithm or AI.

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

• Since this is a physical device, "ground truth" would be defined by engineering specifications, physical measurements (e.g., burst pressure limits), and observed functional performance during bench and animal testing. For the animal study, performance was assessed against "defined user needs" as determined by physicians, which could be considered a form of expert-informed functional assessment.

8. The sample size for the training set:

• Not applicable. The device is not an AI/ML product, so there is no "training set."

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

• Not applicable. No training set exists for this device.

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