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The SpyGlass Discover Retrieval Basket is indicated for the endoscopic removal of stones and stone fragments in the biliary system.

The SpyGlass Discover Retrieval Basket is designed to be used with the SpyGlass Discover Digital Catheter (K200483), which is a single-use endoscope. The SpyGlass Discover Retrieval Basket is a self-expanding nitinol wire basket that is housed within a flexible sheath. The basket is extended from the sheath (opened) and retracted into the sheath (closed) using a thumb slide on the proximal handle. The basket cage is made of four nitinol wire legs. The basket wires are looped at the distal end featuring a tipless design with no exposed wire ends.

The provided text is a 510(k) summary for the Boston Scientific SpyGlass™ Discover Retrieval Basket. This document is a premarket notification to the FDA to demonstrate that a device is substantially equivalent to a legally marketed predicate device. As such, it primarily focuses on demonstrating equivalence through comparison of technical characteristics and performance data, rather than detailing a clinical study with human subjects, acceptance criteria for an AI/algorithm, or MRMC studies that would be typical for an AI-powered diagnostic device.

Therefore, the information requested in your prompt regarding acceptance criteria for an algorithm, study design elements like sample size for training/test sets, expert adjudication, or MRMC studies, and ground truth establishment is not present in this document. This document describes a physical medical device (a retrieval basket), not a software or AI-based diagnostic tool.

However, I can extract the acceptance criteria and performance data for the physical device's bench testing, as described in Section 7, "Performance Data."

Here's a breakdown based on the information available:

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

The document describes various bench tests conducted to evaluate the device. The specific acceptance criteria values and the quantitative results are not explicitly stated in a table format with numerical values within this summary. However, the types of tests and the implicit acceptance criteria (that the device performs acceptably for its intended use and is comparable to the predicate) are listed.

2. Sample size used for the test set and the data provenance:

• Sample Size: Not specified for individual bench tests. The document generically states "Testing includes:" and then lists the tests.
• Data Provenance: The testing was "Non-clinical performance bench testing and simulated use testing" performed by Boston Scientific Corporation. This is laboratory bench data, not human patient data. Therefore, questions of country of origin or retrospective/prospective do not apply in the context of human data studies.

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 as this document solely describes bench testing of a physical device. There is no concept of "ground truth" derived from expert interpretation in this context, nor human subject data requiring expert review.

4. Adjudication method for the test set:

• Not applicable, as there are no human interpretations or judgments to adjudicate in bench testing.

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/software device, nor is it a diagnostic device that would involve human "readers" interpreting images.

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

• Not applicable. This is a physical medical device, not an algorithm.

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

• Not applicable. For bench testing of a physical device, "ground truth" refers to the engineering specifications and measurable physical properties of the device, assessed through specific testing methodologies (e.g., measuring dimensions, tensile strength, successful stone capture in a simulated environment). This is not equivalent to clinical ground truth from patient data.

8. The sample size for the training set:

• Not applicable. There is no "training set" as this is not a machine learning or AI device.

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

• Not applicable. There is no "training set" or "ground truth" in the AI/ML sense.

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