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The AXIOS™ Stent and Delivery System is indicated for use to facilitate transenteric endoscopic drainage of symptomatic pancreatic pseudocysts ≥ 6cm in size, with ≥ 70% fluid content that are adherent to the gastric or bowel wall. Once placed, the AXIOS Stent functions as an access port allowing passage of standard and therapeutic endoscopes to facilitate debridement, irrigation and cystoscopy. The stent is intended for implantation up to 60 days and should be removed upon confirmation of pseudocyst resolution.

The AXIOS™ Stent and Delivery System is an endoscopic device designed to deliver a fully-covered stent between a pancreatic pseudocyst and the gastrointestinal tract. The AXIOS™ Stent is a flexible, MR compatible, fully-covered self-expanding Nitinol stent preloaded within the AXIOS Delivery System. The AXIOS™ Delivery System is compatible with endoscopes equipped with a 3.7mm diameter or larger working channel. The AXIOS Stent and Delivery System is provided sterile, disposable and intended for use during a single patient procedure. The AXIOS Stent and Delivery System are sterilized by a validated method of sterilization via Ethylene Oxide (EO).

The provided document is limited to a 510(k) summary for the AXIOS™ Stent and Delivery System. This type of submission focuses on demonstrating substantial equivalence to a predicate device through non-clinical testing rather than detailed clinical studies with acceptance criteria, performance metrics, and human reader evaluations typical for AI/ML-based diagnostic devices.

Therefore, the information required to populate most of the sections of your request (acceptance criteria, detailed study design with sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC studies, standalone performance, and ground truth establishment for AI/ML models) is not present in this document.

However, I can extract information related to non-clinical testing and the overall conclusion about device performance:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Not Applicable. This document describes non-clinical bench testing and biocompatibility testing, not human clinical trials or AI/ML model performance evaluation with test sets derived from patient data.

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)

• Not Applicable. Ground truth, in the context of expert consensus for AI/ML, is not relevant to this type of device submission. The "ground truth" here is implied by predefined engineering specifications and regulatory standards met through non-clinical laboratory testing.

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

• Not Applicable. Adjudication methods are typically for clinical outcome or diagnostic agreement, which is not described for this non-clinical submission.

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. An MRMC study is not mentioned as this is a medical device for drainage, not an AI-based diagnostic tool.

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

• No. This is a hardware medical device; the concept of "standalone algorithm performance" does not apply.

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

• For the non-clinical testing, the "ground truth" was established by predetermined product specifications, performance characteristics, regulatory standards, and recognized consensus standards (e.g., for biocompatibility and sterilization). The direct comparison was against the predicate device (AXIOS™ Stent and Delivery System K123250) for substantial equivalence.

8. The sample size for the training set

• Not Applicable. This document does not pertain to an AI/ML device that requires a training set.

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

• Not Applicable. As above, no training set for an AI/ML model is described.

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