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The Anchorage™ Scope is a flexible fiber optic endoscope intended for the visualization of body cavities, organs, canals, and tissues. The Anchorage Scope is designed to be introduced through natural body cavities or surgical incisions through introducers, needles or trocars, catheters, sheaths or other devices with lumens having an inside diameter larger than the outside diameter of the endoscope. The Anchorage Scope is indicated for the following applications:

Ureteroscopy Bronchoscopy Thoracoscopy Nasopharyngoscopy/Sinuscopy General laparascopy Urology Gynecology

The Anchorage™ Scope is a flexible fiber optic endoscope provided as a sterile single use device. The endoscope is used to visualize body cavities, organs, canals, and tissues. The Anchorage Scope is introduced through natural body cavities or surgical incisions through introducers or trocars, catheters, sheaths or other devices of with lumens having an inside diameter greater than the outside diameter of the endoscope.

Here's an analysis of the provided text regarding the Epitek Anchorage™ Scope, focusing on the acceptance criteria and supporting study data:

The provided 510(k) summary for the Epitek Anchorage™ Scope does not contain the specific acceptance criteria or a detailed study description that proves the device meets those criteria, as one would typically find for a medical imaging AI device.

Instead, this document describes a traditional medical device (an endoscope) and focuses on demonstrating substantial equivalence to a predicate device based on in-vitro testing, biocompatibility testing, and conformance to consensus and voluntary standards.

Therefore, I cannot populate the requested table or provide answers to most of the specific questions about AI/software performance studies. The information provided is for a hardware device, not an AI or software-based medical device.

However, I can extract the available information:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size for Test Set: Not specified. The document refers to "in-vitro testing" but does not give specific sample sizes for particular tests.
• Data Provenance: Not applicable in the context of clinical data for AI. The testing appears to be laboratory-based (in-vitro) and compliance-based.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable. This is not a study requiring expert ground truth for image interpretation or diagnosis.

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

• Not applicable. This is not a clinical study involving adjudication of interpretations.

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:

• No. This is not an AI device, and therefore no MRMC study with human readers and AI assistance was conducted or reported.

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

• No. This is not an AI algorithm.

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

• Not applicable in the context of clinical ground truth. For the hardware device, the "ground truth" for its performance would be its adherence to engineering specifications, safety standards (e.g., biocompatibility standards), and functional requirements observed during in-vitro testing.

8. The sample size for the training set:

• Not applicable. This is not an AI/machine learning device requiring a training set.

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

• Not applicable.

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