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The Magicore System is intended to replace missing teeth to restore chewing function. The Magicore System can be placed in support of single or multiple-unit restorations including; cement retained, screw retained, or overdenture restorations, and terminal or immediate abutment support for fixed bridgework. This system is for one or two stage surgical procedures. This system is intended for delayed loading.

This submission is to add new fixtures and abutments to the previously cleared device, Magicore System (K201981), Magicore II System (K201621), Magicore II System (K192197), and Magic UCLA Abutment System (K202418).
The fixtures and abutments in this system are below:

1. Fixture

• Magicore
• Magicore (Cutting Edge)

2. Abutment

• Magic Multiunit Abutment (Screw type - Hex, Non-Hex & Cemented type - Hex, Non-Hex)
• Magic Multiunit UCLA Cylinder
• Magic Multiunit Cap
• Magic Abutment (Screw type Hex, Non-Hex & Cemented type Hex, Non-Hex)
• Magic UCLA Cement Retained Type (Hex, Non-Hex)
• Magic Cylinder (Hex, Non-Hex, Post)
• Magic Multiunit Cylinder (Hex, Non-Hex, Post)
• Magicore Healing Cap
• Magicore Healing Cap Screw
• Cylinder Screw
  An endosseous dental implant is a device made of a material such as Ti 6AL 4V Eli (Conforming to ASTM Standard F-136). The Magicore System consists of dental implants, Abutments, cylinders, caps and screws for use in one or two-stage dental implant placement and restorations.
  The implant-Abutment connection is tight and precise fitting with internal hex and Morse taper bevel. The surface of the Magicore implant is treated with RBM (Resorbable Blasted media).

The provided text describes the regulatory submission for the Magicore System, an endosseous dental implant system, and its determination of substantial equivalence (SE) to previously cleared predicate devices. It does not contain information about acceptance criteria or a study proving the device meets those criteria in the context of an AI/ML-driven medical device performance study (e.g., accuracy, sensitivity, specificity, or clinical outcomes).

The document is a 510(k) summary for a dental implant system. The "acceptance criteria" here refers to demonstrating substantial equivalence to a predicate device, as required for FDA 510(k) clearance, rather than performance metrics of an AI model. The studies mentioned are primarily non-clinical (material, mechanical, sterilization, biocompatibility testing) and are leveraged from predicate devices or performed to show equivalence in basic structural and material properties. There is no mention of an AI/ML component in the Magicore System.

Therefore, I cannot provide the requested information about acceptance criteria for AI performance, sample sizes for AI test sets, expert ground truth establishment, MRMC studies, or standalone algorithm performance, as these concepts are not applicable to the provided document.

The document discusses physical and material properties of a dental implant system, comparing them to legally marketed predicate devices to establish substantial equivalence.

However, if we were to interpret "acceptance criteria" in the context of this device's type (dental implants), it would refer to regulatory requirements and engineering performance specifications needed to demonstrate safety and effectiveness. Based on the provided text, here's what can be inferred about the "study" for this traditional medical device type regarding its acceptance for market clearance:

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

Since this is a 510(k) submission for a non-AI/ML dental implant device, the "acceptance criteria" are primarily related to substantial equivalence to predicate devices, material safety, mechanical performance, and sterility. The document outlines comparisons to predicate devices for various characteristics, implying these characteristics meeting equivalent or acceptable standards are the acceptance criteria.

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

• Sample Size: Not applicable in the context of an AI/ML test set. The document refers to non-clinical testing (e.g., sterilization, biocompatibility, fatigue, shelf-life). These tests typically involve a defined number of device units or material samples per standard requirements, not "patient data samples." Specific numbers of units tested are not detailed in this summary, but the tests themselves rely on established sample size methodologies for their respective standards.
• Data Provenance: Not applicable for patient data. The "data" provenance in this context refers to the source of non-clinical test reports, which are largely leveraged from previous 510(k) submissions for predicate devices by the same manufacturer (e.g., K201981, K201621, K192197, K202418, K152520, K173120). These are lab-based tests, not human study data.

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

• Not applicable. This pertains to establishing ground truth for AI model training/testing which is not relevant here. For dental implants, the "ground truth" for material, mechanical, and biological properties is established through adherence to recognized international standards (e.g., ASTM, ISO) and laboratory testing protocols.

4. Adjudication method for the test set:

• Not applicable. This refers to consensus methods for AI/ML ground truth, which is not relevant to this device's clearances.

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 type of study is relevant for diagnostic imaging AI. The document describes a physical dental implant.

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

• Not applicable. No AI algorithm is involved.

7. The type of ground truth used:

• For physical and material properties: The ground truth is based on established engineering principles, material science definitions, recognized industry standards (e.g., ASTM, ISO), and performance specifications determined through laboratory testing (e.g., mechanical strength, biocompatibility, sterility assurance levels).
• For substantial equivalence: The "ground truth" for the FDA's decision is the demonstration that the device's characteristics (intended use, design, materials, etc.) are as safe and effective as a legally marketed predicate device.

8. The sample size for the training set:

• Not applicable. There is no AI model to train.

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

• Not applicable. There is no AI model to train.

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