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The HANAROSTENT® FAST™ Biliary (NNN) is indicated for the palliation of malignant strictures in the biliary tree.

This self-expanding tubular prosthesis is designed to maintain patency of bile duct obstructions caused by malignant tumors. It consists of a self-expandable metal stent and a delivery system. The self-expandable metal stent is made of nickel titanium alloy (Nitinol) wire, and the delivery system is made of polymeric materials. The stent is loaded into the distal part of the delivery system, and expanded in the body by pulling the outer sheath of the delivery system. The HANAROSTENT® FAST™ Biliary (NNN) is intended for single use only.

The provided document is a 510(k) Pre-Market Notification for a medical device called the HANAROSTENT® FAST™ Biliary (NNN). This notification primarily focuses on demonstrating substantial equivalence to a predicate device (HANAROSTENT® Biliary (NNN), K111149) rather than presenting a de novo study with explicit acceptance criteria and device performance for a new, unproven device.

Based on the provided text, here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical acceptance criteria for a clinical study to prove device performance in terms of patient outcomes. Instead, it describes bench testing to confirm the safety and effectiveness of the device compared to a predicate, focusing on technical characteristics and design changes.

The "reported device performance" is essentially that the device performed comparably to the predicate in these bench tests and that the modifications did not raise new safety or effectiveness issues.

2. Sample Size Used for the Test Set and the Data Provenance

The document explicitly states that "Appropriate bench testing was performed" and refers to "non-clinical data." This indicates that no human test set (patients) was used for this 510(k) submission. The testing was performed on physical device samples. The number of samples for each specific bench test is not provided.

• Sample size for test set: Not specified numerically for individual tests, but refers to physical device samples for bench testing. No human test set was used.
• Data provenance: Bench testing results, likely conducted in-house or by a contracted lab. No country of origin for human data, as no human data was used. The study is not applicable for retrospective or prospective classification as it involves bench testing, not clinical data.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts

Not applicable. There was no human "test set" and therefore no ground truth established by medical experts in the context of this 510(k) submission. The ground truth for bench testing is determined by engineering specifications and physical measurements.

4. Adjudication Method for the Test Set

Not applicable. As no human test set was used, there was no need for expert adjudication.

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 510(k) pertains to a physical medical device (stent), not a medical imaging AI algorithm. Therefore, an MRMC study or assessment of AI assistance is not relevant.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Not applicable. This is not an AI algorithm.

7. The Type of Ground Truth Used

The "ground truth" for this submission was based on engineering specifications, physical measurements, and comparison to the predicate device's established performance. The core argument is substantial equivalence, meaning the new device performs similarly to a legally marketed device that has already been deemed safe and effective.

8. The Sample Size for the Training Set

Not applicable. This is not an AI algorithm, so there is no "training set."

9. How the Ground Truth for the Training Set Was Established

Not applicable. This is not an AI algorithm.

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