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BioHorizons Tapered Short Implants are intended for use in the mandible or maxilla as an artificial root structure for single tooth replacement or for fixed bridgework and dental retention. The implants may be restored using delayed loading, or with a terminal or intermediate abutment for fixed or removable bridgework, and for overdentures.

The BioHorizons Tapered Short Implants are machined titanium, screw-form endosseous dental implants supplied in 4.6mm and 5.8mm diameters. The 4.6mm diameter implant includes a 3.5mm prosthetic platform, while the 5.8mm diameter implant includes a 4.5mm prosthetic platform. The implants are provided in 6.0mm and 7.5mm lengths across both diameters. Implant material is titanium alloy as specified in ASTM F136 Standard Specification for Wrought Titanium-6Aluminum-4Vanadium ELI (Extra Low Interstitial) Allov (UNS R56401) for Surgical Implant Applications. The devices are further processed by roughening the threaded surface with Resorbable Blast Texture (RBT) media (tricalcium phosphate) and by micro-machining grooves, known as Laser-Lok® microchannels, on the implant collar. The product is packaged using materials known in the industry to be appropriate for medical device packaging and is provided with a minimum sterility assurance level of 10°, validated in compliance with ANSI/AAMI/ISO 11137-1 Sterilization of healthcare products -- Radiation -- Part 1: Requirements for development, validation and routine control of a sterilization process for medical devices.

The provided document is a 510(k) premarket notification for a medical device, specifically dental implants. It outlines the substantial equivalence of the BioHorizons Tapered Short Implants to legally marketed predicate devices. This type of submission focuses on demonstrating equivalence rather than proving safety and effectiveness through clinical trials in the same way a PMA (Premarket Approval) would.

Therefore, the study design elements typically found for AI/ML device evaluations (such as sample size for test/training sets, expert ground truth establishment, MRMC studies, or standalone algorithm performance) are not applicable to this document as it pertains to a mechanical dental implant, not an AI/ML diagnostic or therapeutic device.

The study described here is primarily bench testing to demonstrate mechanical and material properties, and to verify sterilization and biocompatibility.

Here's the breakdown of the closest applicable information based on your request, understanding the context of a 510(k) for a physical medical device:

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

The document doesn't present a formal "acceptance criteria" table in the way one might for a diagnostic test with metrics like sensitivity/specificity. Instead, it describes performance based on industry standards and comparative testing against predicate devices.

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

• Mechanical Fatigue Testing (ISO 14801): "worst-case (smallest prosthetic platform/smallest implant body diameter combined with the shortest implant length) 4.6mm x 6.0mm Tapered Short Implant was performed". The exact N (number of implants tested) is not specified, but typically for such tests, a statistically relevant number (e.g., n=12 or similar based on ISO standards) would be used.
• Bacterial Endotoxin/Sterilization Validation/Shelf Life: "representative worst-case BioHorizons endosseous dental implant devices" / "representative worst-case BioHorizons dental implant devices". Exact N not specified. "all lots tested" for endotoxins.
• Surface Area/BIC/Axial Pullout: "shortest length worst-case subject implants across both offered implant diameters" compared to "shortest length worst-case primary predicate implants across equivalent implant diameters." Exact N not specified.
• Data Provenance: The studies were conducted internally by BioHorizons or by their A-level supplier. The document does not specify country of origin for the data, but implied to be in compliance with US regulatory requirements (FDA). These are prospective tests performed on manufactured devices.

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 a mechanical device, not imaging data requiring expert interpretation or ground truth establishment. The "ground truth" for these tests comes from physical measurements, chemical analyses, and adherence to validated international standards (ISO, ASTM, USP).

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

• Not applicable. This is relevant for human-interpreted data, not for direct physical measurements and compliance with engineering standards.

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 a study design for AI/ML diagnostic devices, not for mechanical implants.

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

• Not applicable. This refers to AI/ML algorithm performance.

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

• For this device, the "ground truth" is established by:
  • Validated laboratory measurements: e.g., bacterial endotoxin levels, gamma radiation dose verification, elemental analysis (EDX).
  • Adherence to national and international standards: ASTM (e.g., F136, F543), ISO (e.g., 14801, 11137-1), ANSI/AAMI, USP.
  • Comparative analysis against predicate devices: Demonstrating similar or superior performance on relevant mechanical and surface characteristics.
  • Published literature: For claims regarding Laser-Lok® microchannels (Nevins et al. study cited for connective tissue attachment claims).

8. The sample size for the training set:

• Not applicable. This applies to machine learning, not mechanical device testing.

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

• Not applicable. This applies to machine learning, not mechanical device testing.

In summary: The provided document is a 510(k) submission for a physical dental implant. The "acceptance criteria" and "study" are focused on demonstrating substantial equivalence through a series of bench tests and adherence to established material, manufacturing, and sterilization standards, rather than clinical trials or AI/ML performance evaluations.

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The 3i T3® Short Implants are intended for surgical placement in the upper or lower jaw to provide a means for prosthetic attachment in single tooth restorations and in partially or fully edentulous spans with multiple single teeth utilizing delayed loading, or with a terminal or intermediary abutment for fixed or removable bridgework, and to retain overdentures.

The 3i T3® Short Implants are manufactured from commercially pure titanium and feature a roughened apex and traditional OSSEOTITE® coronal surface. In addition, the implants are offered with or without a nano-scale discrete crystalline deposition (DCD®) calcium phosphate (CaP) surface treatment. The dental implants are basic screw-type designs available in parallel walled body geometries with an external hex connection for mating with associated Biomet 3t" external connection restorative components. The implants are offered in 5.0mm and 6.0 mm diameters and 5.0mm and 6.0mm lengths to accommodate varying patient anatomy. Size appropriate cover screws are provided with each implant.

This document is a 510(k) summary for the 3i T3® Short Implants, which are dental implants. The goal of the 510(k) submission is to demonstrate substantial equivalence to legally marketed predicate devices, not to prove clinical effectiveness in a new study. Therefore, the information requested about acceptance criteria, clinical study design, and ground truth for an AI/device performance study is not directly applicable to this document.

However, I can extract the information related to the non-clinical testing that was performed to support the substantial equivalence claim, which includes acceptance criteria and how "performance" was assessed in that context.

Here's the breakdown based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance (Non-Clinical Testing)

Regarding the other requested points, as this is a 510(k) for a physical medical device (dental implant) and not an AI/software as a medical device, much of the requested information does not apply.

• 2. Sample sized used for the test set and the data provenance: For the non-clinical tests, "worst-case" subject devices and worst-case predicate Bicon devices were used. Specific sample numbers are not provided in this summary. Data provenance is not applicable as this is laboratory testing of physical samples.
• 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for non-clinical engineering tests (like fatigue or pullout strength) is based on physical measurements and established standards, not expert clinical consensus.
• 4. Adjudication method for the test set: Not applicable for non-clinical engineering tests.
• 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-assisted device.
• 6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable. This is not an AI-based device.
• 7. The type of ground truth used: For the non-clinical tests, the "ground truth" is derived from physical measurements against established engineering standards (e.g., ISO 14807:2007, ASTM F2052-06e, etc.) and direct comparison to predicate device performance.
• 8. The sample size for the training set: Not applicable. Non-clinical engineering tests do not typically involve a "training set" in the machine learning sense. The "training" for the device design would be part of standard engineering development and simulation, not a data-driven training set.
• 9. How the ground truth for the training set was established: Not applicable.

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