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The ATTUNE Revision Cones are intended for use with the DePuy Revision Knee Systems in a revision total knee replacement surgery for patients suffering from severe pain and disability due to permanent structural damage resulting from rheumatoid arthritis, osteoarthritis, posttraumatic arthritis, collagen disorders, pseudogout, trauma or failed prior surgical intervention.

The ATTUNE Revision cone is to be fixated into ether the proximal tibia or distal femur with or without bone cement. After implantation of the cone, the mating compatible tibial or femoral component is affixed into the using bone cement.

THE POROUS TITANIUM ATTUNE REVISION CONES ARE INTENDED FOR CEMENTED OR CEMENTLESS USE.

Device Description

The ATTUNE® Revision Cones provide supplemental metaphyseal fixation when necessary to make up for either the proximal tibia or distal femur bone loss. The ATTUNE Revision Cones are available in a variety of sizes of Femoral, Concentric, Tibial Bi-Lobe, and Tibial Tri-Lobe configurations. They are compatible with select, commercially available DePuy Orthopaedics tibial base plates and stemmed femoral components.

AI/ML Overview

This document describes a 510(k) premarket notification for the ATTUNE® Revision Cones. It does not involve a device that relies on algorithms, AI, or machine learning, but rather a medical implant. Therefore, the requested information regarding acceptance criteria, study details for AI/algorithm performance, sample sizes for test and training sets, expert qualifications, adjudication methods, MRMC studies, or standalone algorithm performance simply isn't applicable to this submission.

The document focuses on demonstrating substantial equivalence to predicate devices through technical characteristics and non-clinical performance testing for a medical implant, not an AI-driven diagnostic or therapeutic device.

However, I can extract information related to the device's performance and the non-clinical tests conducted to support its substantial equivalence, even if it doesn't align with the typical AI/ML criteria you've provided.

Here's a summary of the non-clinical tests performed, which serve as the "study" proving the device meets its mechanical and material acceptance criteria:

Non-Clinical Performance Data:

Acceptance Criteria / Test	Reported Device Performance	Comments
Cone tibial fatigue testing per ASTM F1800	Performed	Demonstrates durability and resistance to cyclical loading.
Cone tibial and femoral cement pulloff test	Performed	Evaluates the strength of the fixation method with bone cement.
Biocompatibility testing	Performed	Confirms the material's safety for use within the human body.
Particulate Analysis	Performed	Likely assesses wear particles generated from the device, important for long-term implant success.
Magnetic Resonance Imaging (MRI) safety evaluation testing	Performed, concluded no safety issues under specific conditions identified in labeling.	Assesses MRI compatibility, including magnetically induced force, torque, image artifact, and RF heating.
Bacterial Endotoxin Testing per ANSI/AAMI ST 72:2019	Meets the requirement	Ensures the device is free from harmful levels of bacterial endotoxins.

Regarding the specific questions about AI/ML studies:

A table of acceptance criteria and the reported device performance: See table above, adapted for non-AI device.
Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not applicable. These were non-clinical, mechanical/material tests. "Sample size" would refer to the number of devices tested, which is not specified in this summary. Data provenance is not relevant.
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. Ground truth for mechanical tests is typically established by engineering specifications and objective measurements, not expert consensus.
Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. Material and mechanical tests have clear pass/fail criteria, not subjective adjudication.
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 medical implant, not a diagnostic AI.
If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable. This device does not involve an algorithm.
The type of ground truth used (expert concensus, pathology, outcomes data, etc): Not applicable to an AI context. For the non-clinical tests, the "ground truth" is adherence to industry standards (e.g., ASTM F1800, ANSI/AAMI ST 72:2019) and engineering specifications.
The sample size for the training set: Not applicable. There is no training set for a mechanical implant.
How the ground truth for the training set was established: Not applicable. There is no training set.

In conclusion, this document pertains to the regulatory clearance of a physical medical device (knee revision cones) and not an AI/ML-driven product. Therefore, the specific criteria for AI/ML studies are not met, as they are not relevant to this type of device submission. The "studies" conducted were non-clinical tests demonstrating the physical and material safety and performance of the implant.

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