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The Apex Knee™ System is intended for use as a primary or revision total knee replacement. This prosthesis may be used for the following conditions, as appropriate:
• Non-inflammatory degenerative joint disease, including osteoarthritis and avascular necrosis;
• Rheumatoid arthritis;
• Correction of functional deformity;
• Revision procedures where other treatments or devices have failed;
The porous coated femoral component may be used cemented or uncemented (biological fixation). The porous coated tibial baseplate component may be used uncemented (biological fixation). All other femoral, tibial baseplate and patellar components are indicated for cemented use only.
The Apex Knee™ Modular Tibia System Tibial Augments are intended to be bolted to the Tibia Baseplate and cemented to the prepared tibia. The Apex Revision Knee System augments are intended to be bolted to the femoral component and cemented to the prepared femur.

The Apex Revision Knee System includes the Revision Femoral Component, PS-R Insert, Retaining bolts, Femoral Augments and Femoral Stems. The Revision system is compatible with the previously cleared Modular Tibial Baseplates (K101994) and Patella component (K060192). The PS-R Insert has one level of medio-lateral constraint. The Revision Femoral component incorporates a proportionally sized box that is higher than the Apex PS Knee design (K102578). The Revision Femoral Component has the same bone cuts as the Apex CR Knee System and the Apex PS Knee System (K060192 and K102578). Size ranges, high flex and all other key design features of the Apex CR Knee System (K060192) have been retained in the Apex Revision Knee System. The Femoral Augments and Femoral Stems are available for use with the Revision Femoral Component.

The provided text describes the submission of the Apex Revision Knee System for 510(k) clearance. This is a medical device application, not an AI/ML device, and therefore the concepts of "acceptance criteria," "device performance," "sample size for test set," "data provenance," "number of experts," "adjudication method," "MRMC comparative effectiveness study," "standalone performance," "ground truth," and "sample size for training set" in the context of AI/ML models are not directly applicable.

The document focuses on demonstrating substantial equivalence to predicate devices through non-clinical testing.

Here's an interpretation of the request using the information available and noting where AI/ML concepts do not apply:

Description of Acceptance Criteria and Study to Prove Device Meets Acceptance Criteria

The Apex Revision Knee System's acceptance criteria are framed within the context of demonstrating substantial equivalence to legally marketed predicate devices, primarily through non-clinical performance testing. The study supporting this is a series of in vitro mechanical and wear tests conducted according to recognized industry standards.

1. Table of Acceptance Criteria and Reported Device Performance

Given that this is a mechanical medical device, the "acceptance criteria" are implied by adherence to specified ASTM and ISO standards for properties such as flexion range of motion, constraint, contact area, insert post strength, stem testing, augment testing, patello-femoral contact area and stability, and wear. The "reported device performance" is that these tests were conducted and the device "met the acceptance criteria." Specific numerical values for the criteria or performance are not detailed in this summary.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set: The document does not specify the exact number of samples (e.g., knee implants) used for each non-clinical test. It generally refers to "All samples tested."
• Data Provenance: The data provenance is from non-clinical, in vitro laboratory testing performed by the manufacturer, OMNIlife science, Inc. The country of origin of the data is not explicitly stated but is implicitly the United States, where the company is located. The tests are prospective in nature, as they are specifically conducted to support the 510(k) submission.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

• This question is not applicable as the evaluation is based on objective, quantifiable engineering and material science performance test standards (ASTM, ISO) and FDA guidance documents, not on expert interpretation of medical images or patient data.
• The "ground truth" is defined by the requirements of the standards themselves (e.g., specific load conditions, displacement parameters, acceptable wear rates, etc.).

4. Adjudication Method for the Test Set

• This question is not applicable. There is no "adjudication method" in the sense of reconciling differing expert opinions, as the tests are objective mechanical assessments against pre-defined engineering standards. The pass/fail criteria are inherent in the test 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

• This question is not applicable. The device is a mechanical knee implant, not an AI/ML diagnostic or assistive tool for human readers. No MRMC study was conducted.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• This question is not applicable. The Apex Revision Knee System is a mechanical device, not an algorithm. Therefore, "standalone performance" in the context of AI/ML is not relevant.

7. The Type of Ground Truth Used

• The "ground truth" for this device's evaluation is defined by adherence to established engineering and materials science standards (e.g., ASTM F2083-08, ASTM F1223-03, ISO 14243-3, ISO 14243-2, ASTM F1877) and FDA guidance documents for total knee prostheses. These standards specify test methods, conditions, and performance requirements.

8. The Sample Size for the Training Set

• This question is not applicable. As a mechanical medical device, there is no "training set" in the context of machine learning. The device design is based on engineering principles and previous designs, not on statistical learning from a dataset.

9. How the Ground Truth for the Training Set Was Established

• This question is not applicable for the same reasons as #8. There is no training set for this type of device. The "ground truth" for its development is based on established biomechanical knowledge, material science, and the performance characteristics of previously cleared predicate devices.

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