Search Results

The RT-PLUS™ Knee System is a tri-compartmental rotating hinged prosthesis of the total condylar type. The system consists of femoral, tibial and patellar components. It is indicated for use as a replacement of the knee joint in which significant bone loss and/or ligamentous deficiencies have occurred due to tumors, trauma, infection, revision or connective tissue disorders. The RT-PLUS™ Modular Cemented Knee provides joint stability when any or all of the following structures are non-functional: MCL, LCL, PCL, ACL and the iliotibial band.

The RT-PLUS™ Additional Knee Components are identical to the predicate device, except sizes 2 and 10 were added to the existing sizes 4, 6, and 8 of the RT-PLUS™ Knee System.

This 510(k) submission describes a device modification (addition of new sizes) to an existing knee replacement system, the RT-PLUS Knee System. It does not present a study with acceptance criteria and reported device performance in the way typically seen for AI/ML-based medical devices or devices where clinical performance metrics (like sensitivity, specificity, accuracy) are evaluated against a predefined threshold.

Instead, the submission focuses on mechanical performance and substantial equivalence to a predicate device for a mechanical implant.

Here's a breakdown based on the provided document, addressing your questions where applicable, and noting where information is not available due to the nature of the submission:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set (for biomechanical fatigue tests): Not specified beyond "worst-case model." This typically refers to particular configurations or dimensions of the prosthetic components selected to represent the highest stress conditions. It's a physical test, not a data-driven clinical study.
• Data Provenance: Not applicable in the context of clinical data. The tests are biomechanical laboratory tests.

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

• Ground Truth Establishment: Not applicable. Biomechanical tests use engineering principles and standardized testing methodologies. There isn't "ground truth" derived from expert consensus in this type of study.

4. Adjudication method for the test set

• Adjudication Method: Not applicable. Biomechanical tests have objective, measurable outcomes (e.g., cycles to failure, stress levels).

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

• MRMC Study: No. This is a submission for a mechanical implant, not an AI/ML diagnostic or assistive device.

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

• Standalone Performance: No. This is a mechanical implant, not an algorithm.

7. The type of ground truth used

• Ground Truth: For biomechanical fatigue tests, the "ground truth" is based on established engineering standards and methodologies for material and device performance under simulated physiological loading. There is no expert consensus, pathology, or outcomes data used as ground truth for these specific tests.

8. The sample size for the training set

• Training Set: Not applicable. This is not an AI/ML device.

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

• Training Set Ground Truth Establishment: Not applicable.

Summary of the document's relevance to your questions:

This 510(k) submission addresses the modification of an existing knee prosthesis by adding new sizes. The key performance data provided relates to biomechanical fatigue testing of the new components, confirming they meet performance expectations comparative to the predicate device and are "sufficient for in vivo loading." The FDA's review confirms substantial equivalence based on these tests and the device's design.

It's crucial to understand that this submission is for a traditional medical device (a mechanical implant), not for a software-as-a-medical-device (SaMD) or an AI/ML-based device. Therefore, many of your questions, particularly those related to clinical studies, human readers, AI performance metrics, and training data, are not relevant to this specific document or type of device.

Ask a specific question about this device