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The SMART™ Control™ Nitinol Stent Transhepatic Biliary System is intended for use in the palliation of malignant neoplasms in the biliary tree.

The device description of the proposed SMART™ Control™ Nitinol Stent Transhepatic Biliary System is as follows.
• 6 French stent delivery system profile;
• Stent material - Nickel Titanium alloy and tantalum micromarkers;
• Expanded stent diameters 9 and 10 mm;
• Stent lengths: 80 mm;
• Stent delivery system usable length 80 and 120 cm; Guidewire lumen 0.035";
• Proximal Deployment Handle.

The provided text is related to a 510(k) submission for a medical device (SMART™ Control™ Nitinol Stent Transhepatic Biliary System) and describes its substantial equivalence to predicate devices, rather than a study outlining acceptance criteria and detailed device performance metrics.

Therefore, most of the requested information regarding acceptance criteria, study design, sample sizes, expert involvement, and ground truth for an AI/ML device is not applicable or cannot be extracted from the given document.

However, I can provide the following based on the available text:

Acceptance Criteria and Device Performance (Not Applicable - This document is a 510(k) summary for a substantial equivalence determination for a medical stent, not a performance study with explicit acceptance criteria for a new device's efficacy as per your request's format.)

The document focuses on establishing substantial equivalence to predicate devices through pre-clinical testing, rather than defining and proving specific performance metrics against pre-defined acceptance criteria for a novel AI/ML device.

Here's an attempt to answer the questions based on the type of document provided:

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

   • Not applicable. This document is a 510(k) summary for a medical stent, asserting "substantial equivalence" to predicate devices, not reporting performance against explicit, quantifiable acceptance criteria in the manner typically seen for AI/ML diagnostic devices. The equivalence was confirmed through "pre-clinical testing," but no specific metrics or targets are provided in this summary.

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

   • Not applicable. This document does not pertain to a study with a "test set" in the context of AI/ML or diagnostic performance. It refers to "pre-clinical testing" for a physical medical device. No sample size, data provenance, or study type (retrospective/prospective) is mentioned.

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. This document does not describe a study involving expert-established ground truth for a test set.

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

   • Not applicable. This document does not describe a study involving adjudication for a test set.

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 document does not describe an MRMC study or AI assistance.

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/AI device.

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

   • Not applicable. This document does not describe a study requiring ground truth in the context of an AI/ML device. The "pre-clinical testing" likely refers to bench testing, mechanical stress tests, biocompatibility assessments, and potentially animal studies, which establish performance against engineering specifications or biological responses, not a "ground truth" for diagnostic accuracy.

8. The sample size for the training set

   • Not applicable. This document does not describe a training set as it is not about an AI/ML device.

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

   • Not applicable. This document does not describe a training set or its ground truth.

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