Search Results

The Xceed Nitinol Self-Expanding Transhepatic Biliary Stent System is intended for use in the palliation of malignant neoplasms in the biliary tree.

The Abbott Vascular Devices Xceed Nitinol Self-Expanding Transhepatic Biliary Stent System is comprised of a delivery system and a self-expanding stent. The delivery system is an over the wire (OTW) system designed to deliver the self-expanding stent to the biliary tree.

This document is a 510(k) summary for the Xceed Nitinol Self-Expanding Transhepatic Biliary Stent System. It is a premarket notification for a medical device seeking clearance from the FDA.

Based on the provided text, there is no information about acceptance criteria or a study that proves the device meets specific acceptance criteria related to performance metrics like accuracy, sensitivity, or specificity.

Here's a breakdown of why and what information is available:

1. A table of acceptance criteria and the reported device performance:

• Not Applicable. The document is a 510(k) premarket notification for a medical device (a stent and its delivery system), not an AI/algorithm-driven device. As such, it does not typically involve acceptance criteria related to performance metrics like those for AI. The "performance" assessment for a stent primarily revolves around non-clinical testing (material properties, mechanical integrity, deployment success, etc.) and clinical safety and effectiveness in its intended use.
• The document states: "Substantial equivalence was confirmed through non-clinical testing." This refers to a comparison with a legally marketed predicate device (K050103) based on design characteristics, materials, and non-clinical performance (e.g., bench testing) to demonstrate that the new device is as safe and effective as the predicate.

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 information is relevant for studies involving data analysis (like AI/ML performance). For a stent 510(k), non-clinical testing involves bench tests and potentially animal studies, not typically "test sets" of patient data in the AI sense. Clinical data, if submitted, would be described in terms of patient cohorts. However, this 510(k) specifically only mentions "non-clinical testing" for substantial equivalence.

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. No ground truth establishment by experts for a "test set" is mentioned, as this is not an AI/algorithm device submission.

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

• Not Applicable. No adjudication method is described, as there's no "test set" in the context of an AI/ML algorithm requiring expert adjudication of results.

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. Therefore, no MRMC study comparing human readers with and without AI assistance was conducted or reported.

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

• Not Applicable. This is a physical medical device (stent), not a standalone algorithm.

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

• Not Applicable. As mentioned, this is not an AI/ML device requiring ground truth for data analysis. The "truth" for a stent's performance is typically assessed through its mechanical properties, biocompatibility, and clinical outcomes for safety and effectiveness in its intended use. The 510(k) relies on showing substantial equivalence to a predicate device, meaning its characteristics and expected performance are similar enough to the device already on the market.

8. The sample size for the training set:

• Not Applicable. This information pertains to machine learning models, which are not relevant to this device.

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

• Not Applicable. As above, this is not a machine learning device.

In summary: The provided document is a 510(k) summary for a physical medical device (a stent), not an AI/ML device. Therefore, the questions related to acceptance criteria, test sets, ground truth establishment, and AI performance metrics are not applicable to the information contained within this specific regulatory submission. The device received 510(k) clearance based on "non-clinical testing" demonstrating "substantial equivalence" to a predicate device.

Ask a specific question about this device