Search Results

The ACS® LD Uni FB Knee System is indicated for partial replacement of the articulating surfaces of the knee when only one side of the joint is affected due to compartmental primary degenerative or post-traumatic degenerative disease, previous tibial condyle or plateau fractures, deformity or revision of previous arthroplasty.

This device is single-use implant intended for implantation with bone cement.

The ACS® LD Uni FB Knee System is a unicondylar knee replacement system. It is intended for patients with unicompartmental osteoarthritis and intact cruciate and collateral ligaments.

The ACS® LD Uni FB Knee System consists of the following components:

• ACS® Uni LD Femoral Component .
• Uni FB Tibial Component ●
• Uni FB PE-Insert ●

The provided text is a 510(k) summary for a medical device (ACS® LD Uni FB Knee System), which is a knee replacement system. This document focuses on demonstrating substantial equivalence to predicate devices through design, materials, and performance testing, rather than establishing acceptance criteria and proving performance through a clinical study that would be typical for an AI/ML device or a novel diagnostic.

Therefore, the information required to populate the fields about acceptance criteria, study design, expert involvement, and ground truth, as typically found in submissions for AI/ML or diagnostic devices, is not present in this document. The provided text outlines engineering and material performance tests for a physical implant, not a data-driven system.

However, I can extract the information that is present and explain why other requested information is not applicable to this type of device submission.

Here's how to interpret the request in the context of the provided document:

Acceptance Criteria and Device Performance (as applicable to a physical implant):

The document doesn't define specific "acceptance criteria" in the sense of accuracy metrics (e.g., sensitivity, specificity) for a diagnostic device. Instead, "acceptance criteria" are implied by the successful completion of specified engineering and mechanical performance tests, demonstrating that the device performs as intended and is substantially equivalent to predicate devices.

Notes on the Study (as applicable to a physical implant, not an AI/ML system):

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

   • Sample Size: Not explicitly stated as "sample size" in terms of patient data. The testing was performed on "test units representative of finished devices." The exact number of physical units tested per standard is not detailed.
   • Data Provenance: Not applicable in the sense of patient data. The "data" comes from bench testing of physical implant components.

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

   • Not Applicable. This is a mechanical device, not a diagnostic or AI/ML system requiring expert-adjudicated ground truth from medical images or clinical outcomes. Ground truth for mechanical testing is established by engineering standards and measurements.

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

   • Not Applicable. No human adjudication of results in the clinical or diagnostic sense. Tests follow standardized procedures (ASTM).

4. 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 physical implant, not an AI/ML diagnostic or assistive device.

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

   • Not Applicable. This is a physical implant.

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

   • Engineering/Mechanical Standards. The "ground truth" is defined by the requirements and performance limits set by the referenced ASTM (American Society for Testing and Materials) standards for knee implants.

7. The sample size for the training set:

   • Not Applicable. There is no "training set" for a physical implant. The design and manufacturing processes are developed based on engineering principles and material science, not machine learning training data.

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

   • Not Applicable. (See point 7)

Summary regarding the provided document:

This document describes a 510(k) submission for a physical medical device (a knee implant). The FDA's review for such devices primarily focuses on demonstrating substantial equivalence to existing legally marketed predicate devices. This involves:

• Similar intended use.
• Similar technological characteristics (materials, design).
• Performance testing (bench testing, not clinical trials or AI/ML evaluations) to demonstrate the device performs as intended and meets relevant engineering standards.

The questions provided in the prompt are highly tailored to the evaluation of AI/ML-based medical devices or diagnostic devices that rely on interpreting clinical data, images, or signals. Since the provided text pertains to a mechanical orthopedic implant, most of these questions are not relevant or applicable to its regulatory clearance process as demonstrated in this 510(k) summary.

Ask a specific question about this device