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Endo-Glide Guidewires are intended to be used for selective cannulization of the biliary ducts, including but not limited to, the common bile, cystic, right and left hepatic ducts during endoscopic biliary procedures for catheter introduction and exchanges.

The Endo-Glide Guidewire has a range of diameters from 0.025" to 0.035" and lengths from 250cm to 450cm. The wires are offered with and without a 50mm radiopaque platinum distal tip (curved and straight). The wires are assembled with a solid nitinol or stainless steel core wire, which can be tapered at the distal tip. A lubricious coating (PTFE or hydrophilic) surrounds the entire core and/or tip, which also provides insulation.

This is a 510(k) premarket notification for a guidewire, which is a physical medical device, not an AI/ML powered device. As such, the concepts of "acceptance criteria," "device performance study," "sample size," "ground truth," "MRMC study," "standalone study," "training set," and "adjudication method" as they relate to AI/ML device evaluations are not applicable in this context.

The document discusses the substantial equivalence of the Endo-Glide Guidewire to predicate devices based on technological characteristics, intended use, design, materials, and method of operation. It does not contain information about the performance criteria, study design, or results in the way it would for an AI/ML device.

Here's a breakdown of what is available in the document, framed to address your request where possible, but highlighting the inapplicability to AI/ML:

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

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 document pertains to the substantial equivalence of a physical medical device (guidewire) to legally marketed predicate devices. The concept of a "test set" and "data provenance" in the context of performance evaluation for an AI/ML device is not relevant here. The evaluation focuses on physical characteristics and intended use.

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. There is no "ground truth" to establish for a test set in the AI/ML sense for this type of device submission. The FDA reviews the device's design, materials, manufacturing processes, and intended use for substantial equivalence, relying on established standards for medical devices and comparison to approved predicates.

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

• Not applicable. No adjudication method for a test set is mentioned or relevant for this type of device.

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/ML device, so no MRMC study, human readers, or AI assistance is involved.

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

• Not applicable. This is a physical device, so "standalone algorithm performance" is not a relevant concept.

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

• Not applicable. Ground truth, in the context of AI/ML, is not relevant for this device. The "truth" for substantial equivalence is based on engineering principles, materials science, and comparison to legally marketed predicate devices.

8. The sample size for the training set

• Not applicable. This is not an AI/ML device, so there is no training set.

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

• Not applicable. As there is no training set, there is no ground truth for a training set to be established.

In summary, the provided document is a 510(k) clearance letter for a physical guidewire, not an AI/ML-driven medical device. Therefore, the questions related to AI/ML performance evaluation, such as acceptance criteria, sample sizes for test/training sets, ground truth establishment, and expert involvement for algorithm validation, are not applicable to the information contained within this document. The "study" here is essentially the comparison of the new device's characteristics to those of already approved predicate devices to demonstrate substantial equivalence.

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