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510(k) Data Aggregation
(282 days)
MUTARS Cemented Femoral Stems
The MUTARS® Proximal Femur Replacement System is a modular hip replacement system offering various components that can be combined to replace the hip joint and address major bone defects with various options depending upon the size and location of the defects of each patient.
The MUTARS® Proximal Femur System is intended for cemented use in total hip arthroplasty or hemiarthroplasty for the following indications:
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Proximal femur replacement in oncology cases where radical resection and replacement of bone is required.
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Limb salvage procedures including surgical intervention for severe trauma, failed previous prosthesis, and/or oncology indications, where radical resection and replacement of the bone is required.
Use of the prosthesis is generally only indicated in skeletally mature patients.
The purpose of this Traditional 510(k) is to expand the MUTARS® Proximal Femur System with a line extension to the existing cementless femoral stems. The new stems are intended for cemented applications.
This is a 510(k) summary for a medical device (MUTARS® Cemented Femoral Stems), not a study report detailing AI device performance. As such, most of the requested information about acceptance criteria, study design, and AI performance metrics is not applicable or available in this document.
However, I can extract the information relevant to the device's performance testing and the conclusion of substantial equivalence.
1. Table of Acceptance Criteria and Reported Device Performance
Since this document describes a traditional 510(k) for a medical implant and not an AI/software device, the "acceptance criteria" and "reported device performance" are related to mechanical and material properties rather than typical AI performance metrics like sensitivity, specificity, or AUC. The document states that testing was performed to assure substantial equivalence and demonstrate the subject devices perform as intended.
Acceptance Criteria (General Purpose) | Reported Device Performance |
---|---|
Assure substantial equivalence | Achieved |
Perform as intended | Achieved |
Meet ISO 7206-4 for Distal Stem Fatigue | Testing performed |
Particle Analysis | Testing performed |
2. Sample size used for the test set and the data provenance
The document states: "All testing was performed on test units representative of the finished device." It does not specify the exact sample size for the mechanical testing (e.g., number of stems tested for fatigue) or the data provenance (e.g., country of origin, retrospective/prospective).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable. This is not an AI/diagnostic device that requires expert ground truth establishment for performance evaluation in the way a clinical study would. The performance is based on engineering and material testing.
4. Adjudication method for the test set
Not applicable. This is not an AI/diagnostic device requiring adjudication for ground truth.
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. This device is a physical medical implant, not an AI-assisted diagnostic tool for human readers.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
Not applicable. This device is a physical medical implant, not an algorithm.
7. The type of ground truth used
For this type of device, the "ground truth" for performance would be established by:
- Engineering specifications and standards: Adherence to established mechanical and material properties as defined by relevant industry standards (e.g., ISO 7206-4 for fatigue testing).
- Predicate device characteristics: Comparison to the known performance and characteristics of the legally marketed predicate device (MUTARS® Proximal Femur System (K181778)) to establish substantial equivalence.
8. The sample size for the training set
Not applicable. This device does not involve a "training set" in the context of machine learning or AI.
9. How the ground truth for the training set was established
Not applicable.
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