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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.

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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.

The provided document is a 510(k) summary for the Xceed Nitinol Self-Expanding Transhepatic Biliary Stent System. It details the device, its intended use, and its substantial equivalence to a predicate device. However, it does not contain information about acceptance criteria or a study proving the device meets acceptance criteria in the way typically associated with AI/software devices (e.g., performance metrics, ground truth, expert adjudication).

This document is for a physical medical device (a stent and delivery system), and the concept of "acceptance criteria" for its performance is addressed through different types of testing, primarily non-clinical (mechanical, materials, functional) testing, rather than studies involving performance metrics like sensitivity, specificity, or reader improvement.

Therefore, I cannot fulfill most of your request as the information is not present in the provided text. I can, however, extract the information that is available:

1. A table of acceptance criteria and the reported device performance:
This information is not provided in the document. The document states:

• "Substantial equivalence was confirmed through non-clinical testing." This implies that the device met certain internal or regulatory criteria for physical and functional performance, but the specific metrics, acceptance thresholds, and measured results are not detailed.

2. Sample size used for the test set and the data provenance:
This information is not applicable/provided in the context of this 510(k) summary for a physical stent system. There is no "test set" of data in the sense of images or cases. "Non-clinical testing" typically involves bench testing, material characterization, and perhaps animal models, not human data sets.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
This information is not applicable/provided. As noted above, there isn't a "test set" requiring expert ground truth in the context of this submission.

4. Adjudication method for the test set:
This information is not applicable/provided.

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:
This information is not applicable/provided. MRMC studies are relevant for software/AI devices where human reader performance is being evaluated, often with and without AI assistance. This document is for a physical stent.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
This information is not applicable/provided. This is a physical device, not an algorithm.

7. The type of ground truth used:
This information is not applicable/provided. For a physical device like a stent, "ground truth" would relate to its physical properties meeting specifications, achieved through engineering and quality control, rather than expert consensus on diagnostic imaging.

8. The sample size for the training set:
This information is not applicable/provided. There is no "training set" as this is not an AI/machine learning device.

9. How the ground truth for the training set was established:
This information is not applicable/provided.

Summary of available information from the document (relevant to the device itself):

• Device Trade Name: Xceed Nitinol Self-Expanding Transhepatic Biliary Stent System
• Intended Use: The Xceed Nitinol Self-Expanding Transhepatic Biliary Stent System is intended for use in the palliation of malignant neoplasms in the biliary tree.
• Predicate Device: Xceed Nitinol Self-Expanding Transhepatic Biliary Stent System (K032929)
• Proof of Substantial Equivalence: "Substantial equivalence was confirmed through non-clinical testing."
• FDA Labeling Limitation: "The safety and effectiveness of this device for use in the vascular system have not been established." And the biliary use indication must be prominently displayed.

In essence, the document serves as a regulatory submission for a physical medical device, not an AI or software-based diagnostic tool, which is why the specific metrics and study designs you've asked for are not present.

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