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Niti-S Esophageal Stent is intended for use in esophageal strictures caused by intrinsic and/or extrinsic malignant tumors.

The proposed Niti-S Esophageal Stent consists of an implantable metallic stent and a flexible introducer system. The stent is a rigid, flexible, and expandable tubular device made of a Nitinol wire that is intended to be implanted to restore the structure and/or function of the esophagus. This device also includes the introducer. Upon deployment, the stent imparts an outward radial force on the luminal surface of the duct to establish patency.

The provided text is a 510(k) summary for the Niti-S Esophageal Stent, focusing on its substantial equivalence to predicate devices. It describes the device, its intended use, and technological characteristics. However, it does not contain the detailed performance data or study information required to fill out the requested table and answer many of your questions.

Specifically, this document is a regulatory submission for premarket notification, which often relies on demonstrating substantial equivalence to existing devices rather than extensive new clinical trials or detailed performance studies with acceptance criteria in the same way an AI/ML device might.

Here's what can be extracted and what cannot:

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

The document states: "Laboratory testing regarding characteristics was performed on the Niti-S Esophageal Stent to verify its safety and performance." and "A biocompatibility assessment was performed on the patient contact materials." However, it does not provide any specific acceptance criteria (e.g., in terms of sensitivity, specificity, accuracy, or a specific metric) or the reported performance metrics (e.g., actual values from tests like radial force, fatigue, etc.).

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

This information is not provided in the document. The general statement "Laboratory testing regarding characteristics was performed" does not specify sample sizes or data provenance.

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)

This information is not provided. The document describes physical device testing (laboratory testing and biocompatibility), not studies involving expert evaluation to establish ground truth for a test set in the way an AI/ML device would require.

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

This information is not 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 provided. This device is an esophageal stent, not an AI-powered diagnostic or assistive tool, so an MRMC study comparing human readers with and without AI assistance is not applicable.

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

This is not applicable as the device is a physical stent, not an algorithm.

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

For a physical medical device like an esophageal stent, "ground truth" would typically relate to predefined engineering specifications, material properties, and biological compatibility standards. The document mentions "Laboratory testing regarding characteristics" and "biocompatibility assessment," implying that the device was evaluated against these types of established standards, but it does not explicitly detail the "type of ground truth" in a way that aligns with AI/ML device evaluations.

8. The sample size for the training set

This is not applicable. This document is for a physical medical device and discusses laboratory testing and biocompatibility, not an AI/ML model that requires a training set.

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

This is not applicable.

In summary:

This 510(k) submission primarily focuses on demonstrating substantial equivalence to predicate devices based on intended use, technological characteristics, and general safety/performance statements from laboratory testing and biocompatibility. It does not provide the detailed performance metrics, study designs, expert involvement, or AI-specific information requested in your prompt. This type of submission generally relies on demonstrating that the new device is as safe and effective as a legally marketed predicate, rather than presenting novel clinical trial data with specific acceptance criteria and ground truth establishment, as would be expected for a new AI/ML device.

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