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For use in edentulous mandibles or maxillae for attachment of complete denture prostheses, or as a terminal or intermediary abutment for fixed or removable bridgework, or as a free standing single tooth replacement.

These Class III dental implants are supplied sterile, ready for placement by a licensed dentist. There has been no significant change to the prosthetic components.

This document is a 510(k) summary for a dental implant system (ThreadLoc Dental Implant System). It serves to demonstrate substantial equivalence to a predicate device. The information provided is not relevant for characterizing device performance based on acceptance criteria from a clinical or diagnostic study for an AI-powered device.

Here's why and what information is missing based on your request:

• Device Type: This is a physical medical device (dental implant), not an AI-powered diagnostic or assistive technology. The acceptance criteria and performance metrics you're asking about (e.g., sensitivity, specificity, AUC, human reader improvement with AI) are specific to AI/software as a medical device (SaMD) used for analysis, diagnosis, or prediction.
• Study Type: A 510(k) summary primarily focuses on demonstrating "substantial equivalence" to a legally marketed predicate device, often through technological characteristics and intended use, rather than presenting clinical trial data that establishes performance against specific acceptance criteria in the way an AI/SaMD device would.
• Data Provided: The document details changes to the physical implant (self-tapping capabilities, surface textures) and states the intended use is identical to the predicate. It does not contain any performance metrics for diagnostic accuracy, classification, or human reader improvement.

Therefore, I cannot populate the table or answer the specific questions about acceptance criteria, performance metrics, sample sizes, ground truth, or MRMC studies because the provided document does not contain this type of information for an AI/SaMD.

To answer your request for an AI-powered device, the input document would need to describe a study evaluating the diagnostic or assistive performance of an AI algorithm, including details like:

• Clinical study design and outcomes.
• Performance metrics (e.g., sensitivity, specificity, accuracy, F1-score, AUC).
• Pre-defined acceptance criteria for these metrics.
• Details about the test dataset (size, provenance, ground truth establishment, adjudication).
• Information on training data and methods.
• Results from standalone performance (if applicable) or human-in-the-loop studies (e.g., MRMC studies).

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