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Advanix™ Biliary Stent with NaviFlex™ RX Delivery System is intended for delivery of the stent to the biliary tract for drainage of the bile ducts, for splinting of a bile duct during healing, or for providing bile duct patency in a stricture or past a stone.

Device Description

The Advanix™ Biliary Stent with NaviFlex™ RX Delivery System consists of biliary plastic stents and delivery system catheters. They are sold separately or in a pre-loaded configuration, in which a stent comes attached to the catheter via a suture. The system is comprised of two (2) primary components: stent and delivery catheter with a locking mechanism. The Advanix Biliary Stent allows for drainage of the biliary duct by preventing closure and maintaining the patency of the biliary duct. These biliary stents are provided in center bend and duodenal bend shapes and have leading and trailing bars, a tapered leading end tip to facilitate access through the papilla, and a rounded tailing end to match the profile of the push catheter portion of the delivery system.

AI/ML Overview

This document is a 510(k) Summary for a medical device (Advanix Biliary Stent with NaviFlex RX Delivery System) and does not describe a study involving an AI/Machine Learning device or comparative effectiveness with human readers. Therefore, many of the requested categories are not applicable.

Here's an analysis of the provided text based on the specific questions, focusing on what can be extracted:

The document is a 510(k) premarket notification for a medical device (Advanix Biliary Stent with NaviFlex RX Delivery System). It discusses the device's characteristics, indications for use, and performance data to demonstrate substantial equivalence to a predicate device (K101314).

Crucially, this is NOT a document about an AI device or a study involving AI/Machine Learning performance. Therefore, many of the questions regarding AI-specific elements (like human reader improvement with AI, standalone AI performance, ground truth for test/training sets in an ML context, sample sizes for ML sets, multi-reader multi-case studies) are not relevant to this content.

However, I can extract information related to the device's non-clinical performance and acceptance criteria.

Acceptance Criteria and Device Performance Study (Non-AI Device)

1. Table of acceptance criteria and reported device performance:

The document broadly states that "All tests met required acceptance criteria, therefore supporting a finding of substantial equivalence." However, it does not provide a specific table with numerical acceptance criteria and corresponding reported performance metrics. It lists the types of tests performed.

Test Category	Specific Test Performed	Acceptance Criteria (General Statement)	Reported Device Performance (General Statement)
Biocompatibility	MEM Elution Cytotoxicity	Aligned with ISO 10993, ISO 11135-1, ISO 10993-7, and FDA Guidance document.	Results meet requirements.
	Guinea Pig Maximization	Aligned with ISO 10993, ISO 11135-1, ISO 10993-7, and FDA Guidance document.	Results meet requirements.
	Intracutaneous Reactivity	Aligned with ISO 10993, ISO 11135-1, ISO 10993-7, and FDA Guidance document.	Results meet requirements.
	Acute Systemic Injection	Aligned with ISO 10993, ISO 11135-1, ISO 10993-7, and FDA Guidance document.	Results meet requirements.
	Implantation	Aligned with ISO 10993, ISO 11135-1, ISO 10993-7, and FDA Guidance document.	Results meet requirements.
Chemical Extractables	Studies on chemical extractables	Aligned with FDA's Guidance document, "Use of International Standard ISO 10993-1".	Results meet requirements.
Bench Testing	Drainage Lumen ID	Aligned with FDA guidance document, "Recommended Content and Format of Non-Clinical Bench Performance Testing Information in Premarket Submissions".	All tests met required acceptance criteria.
	Stent Length	Aligned with FDA guidance document, "Recommended Content and Format of Non-Clinical Bench Performance Testing Information in Premarket Submissions".	All tests met required acceptance criteria.
	Stent OD	Aligned with FDA guidance document, "Recommended Content and Format of Non-Clinical Bench Performance Testing Information in Premarket Submissions".	All tests met required acceptance criteria.
	Stent Shape	Aligned with FDA guidance document, "Recommended Content and Format of Non-Clinical Bench Performance Testing Information in Premarket Submissions".	All tests met required acceptance criteria.
	Repositionability (pre-loaded system only)	Aligned with FDA guidance document, "Recommended Content and Format of Non-Clinical Bench Performance Testing Information in Premarket Submissions".	All tests met required acceptance criteria.
	Deployment Force	Aligned with FDA guidance document, "Recommended Content and Format of Non-Clinical Bench Performance Testing Information in Premarket Submissions".	All tests met required acceptance criteria.
	Device Trackability Force	Aligned with FDA guidance document, "Recommended Content and Format of Non-Clinical Bench Performance Testing Information in Premarket Submissions".	All tests met required acceptance criteria.
	Guidewire Compatibility	Aligned with FDA guidance document, "Recommended Content and Format of Non-Clinical Bench Performance Testing Information in Premarket Submissions".	All tests met required acceptance criteria.
	Duodenoscopy Compatibility	Aligned with FDA guidance document, "Recommended Content and Format of Non-Clinical Bench Performance Testing Information in Premarket Submissions".	All tests met required acceptance criteria.
	Barb Flap Cover Compatibility	Aligned with FDA guidance document, "Recommended Content and Format of Non-Clinical Bench Performance Testing Information in Premarket Submissions".	All tests met required acceptance criteria.
	Tensile: Aged in Bile	Aligned with FDA guidance document, "Recommended Content and Format of Non-Clinical Bench Performance Testing Information in Premarket Submissions".	All tests met required acceptance criteria.
	Lumen Patency	Aligned with FDA guidance document, "Recommended Content and Format of Non-Clinical Bench Performance Testing Information in Premarket Submissions".	All tests met required acceptance criteria.

2. Sample size used for the test set and the data provenance:

Sample Size: Not specified. The document only mentions that bench tests were performed but does not quantify the number of devices or components tested.
Data Provenance: Not specified. The tests are bench tests performed on the device itself (parts of the device).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

Not applicable. This is not a study requiring expert ground truth for image interpretation or diagnosis. The "ground truth" for these tests would be the physical properties and performance characteristics of the device measured against predetermined specifications.

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

Not applicable. This concept is for clinical or radiological studies with expert review.

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 a traditional medical device (biliary stent), not an AI diagnostic or assistive device.

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

Not applicable. This refers to AI algorithm performance.

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

For biocompatibility: Ground truth is established by adherence to international standards (ISO 10993, ISO 11135-1, ISO 10993-7) and FDA guidance for biological evaluation and sterilization residuals.
For bench testing: Ground truth is implicitly defined by the specified engineering and performance requirements for the device, and adherence to FDA guidance for non-clinical bench performance testing.

8. The sample size for the training set:

Not applicable. This is not an AI/Machine Learning device, so there is no training set in that context.

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

Not applicable. No training set for an AI model.

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