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Indications for the Biomet® Modular Revision Femoral System Include:

1. Noninflammatory degenerative joint disease including osteoarthritis and avascular necrosis.
2. Rheumatoid arthritis.
3. Correction of functional deformity.
4. Treatment of non-union, femoral neck fracture and trochanteric fractures of the proximal femur with head involvement, unmanageable using other techniques.
5. Revision of previously failed total hip arthroplasty.
   The Biomet® Modular Revision Femoral System hip components are single-use implants, intended for uncemented applications.

The Blomet® Modular Revision Femoral System is a comprehensive, press-fit revision stem design that provides the surgeon with multiple styles of modular proximal and distal bodies for reconstruction of various defects commonly seen in femoral revision surgery. The proximal bodies will consist of broached, calcarreplacing, and cone-style implants. The system also includes auxiliary implants to aid in fixation. A single set of instrumentation is provided for all styles. The system is intended for uncemented applications

The provided document K090757 is a 510(k) summary for the Biomet® Modular Femoral Revision System. It describes a medical device, specifically a hip implant system, and its substantial equivalence to previously marketed devices.

Based on the content of K090757, I cannot provide details on "acceptance criteria" for software performance or "study that proves the device meets the acceptance criteria" in the context of AI/ML-driven medical devices. This document pertains to a traditional orthopedic implant, and the regulatory submission relies on non-clinical testing and substantial equivalence to predicate devices, rather than clinical efficacy studies or performance metrics for an AI algorithm.

Here's why and what information can be extracted:

Why the requested information cannot be fully provided:

• No AI/ML Component: The device is a physical femoral revision system (hip implant). There is no indication of any AI or machine learning component.
• Focus on Substantial Equivalence: The 510(k) process for this type of device focuses on demonstrating that the new device is as safe and effective as a legally marketed predicate device. This is primarily done through technological characteristics comparison and non-clinical (e.g., mechanical, material) testing, not clinical studies with performance metrics like sensitivity, specificity, or reader improvement.
• No Clinical Testing: The document explicitly states under "Clinical Testing": "None provided as a basis for substantial equivalence." This means no human clinical trials were conducted to assess the device's performance against specific clinical acceptance criteria.

Information that can be extracted from the document related to the study (in a general sense of "evaluation"):

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

   • Acceptance Criteria: Implicitly, the acceptance criterion for a 510(k) submission not requiring clinical data is that the device demonstrates substantial equivalence to predicate devices through technological characteristics and non-clinical testing, ensuring it is as safe and effective. The document states, "The results indicated that the device was functional within its intended use."
   • Reported Device Performance: The document doesn't provide specific performance metrics in a table format. It generally states that "Non-clinical laboratory testing was performed to determine substantial equivalence. The results indicated that the device was functional within its intended use."

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

   • Not applicable. No "test set" in the context of an AI/ML algorithm was used. Non-clinical laboratory testing would involve mechanical or material samples, but specific numbers and provenance are not detailed in this summary.

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

   • Not applicable. No "ground truth" for a test set was established by human experts in the context of an AI/ML device.

4. Adjudication method for the test set:

   • Not applicable. No "adjudication method" for interpreting results from a test set of data was performed for an AI/ML device.

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. No clinical studies, especially MRMC studies comparing human performance with and without AI assistance, were performed or described.

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

   • Not applicable. No algorithm is involved.

7. The type of ground truth used:

   • Not applicable in the AI/ML sense. For this orthopedic device, the "ground truth" for safety and effectiveness is largely established by adherence to engineering standards, material properties, and comparison to the known performance of predicate devices through non-clinical testing.

8. The sample size for the training set:

   • Not applicable. There is no AI/ML algorithm that would require a "training set."

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

   • Not applicable.

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