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All Exactech Hip Systems are indicated for use in skeletally mature individuals undergoing primary surgery for hip replacement due to osteoarthritis, rheumatoid arthritis, osteonecrosis, post-traumatic degenerative problems of the hip, and for treatment of proximal femoral fractures where prosthetic replacement is determined by the surgeon as the preferred treatment. Components of Exactech Hip Systems are also potentially indicated for ankylosing spondylitis, congenital hip dysplasia, revision of failed previous reconstructions where sufficient bone stock is present, and to restore mobility resulting from previous fusion.

• Cemented femoral stems and cemented acetabular cups are intended for cemented fixation only.
• Press-fit femoral stems and acetabular cups are intended for press-fit fixation.
• Femoral heads and endoprostheses are intended for use in cemented and press-fit applications.

The Novation LPI Prime Femoral Stem is a titanium press-fit prosthesis featuring a 12/14 trunnion that is used on the femur side of a total or hemi hip arthroplasty. The proximal region of the stem is coated with porous titanium plasma spray for uncemented, biological fixation.

The proposed femoral stems are intended to mate with the following modular 12/14 femoral heads:

• Exactech Cobalt Chromium Alloy Femoral Heads (K041906) .
• Exactech Ziramic Zirconia Femoral Heads (K050398, K060107) .
• Exactech BIOLOX®forte Alumina Femoral Heads (K032964, K051682) .
• Exactech BioloxDelta and DeltaOption Femoral Heads and Adapters . (K103012)
• AcuMatch L-series Unipolar endoprosthesis (K010081)

The proposed femoral stems are intended to mate with the following bipolar components:

• AcuMatch L-Series Bipolar Endoprosthesis (K013211) .

The provided document is a 510(k) summary for the Exactech® Novation® LPI Prime Femoral Stem, a medical device. It does not describe an AI/ML device or present a study comparing AI performance against acceptance criteria in the typical sense of a diagnostic or predictive algorithm.

Therefore, the requested information about acceptance criteria, device performance in terms of metrics like sensitivity/specificity, sample sizes for test/training sets, ground truth establishment, expert adjudication, or MRMC studies for AI/ML performance is not applicable to this document.

Instead, this document describes the regulatory submission for a physical medical implant (a femoral stem for hip replacement) and establishes its substantial equivalence to previously marketed devices based on technological characteristics and mechanical testing.

Here's a breakdown of the relevant information from the document, rephrased to fit the context of a medical device submission, rather than an AI/ML study:

1. Table of Acceptance Criteria and Reported Device Performance (Non-AI Context)

• Clinical Literature Review of similar femoral prostheses.
• Cadaver lab validation for design features.
• Finite Element Analyses for worst-case size.
• Range of Motion testing per EN ISO 21535.
• Distally Fixed Fatigue Worst case Press-fit Fatigue Testing per FDA guidance.
• Femoral Neck Fatigue Worst case Press-fit Fatigue Testing per FDA guidance.
• Femoral Head Modular Junction Burst Testing and Axial pull-off Testing on the taper specification. |
  | Intended Use is similar to predicate devices. | Similar indications for use as predicate devices (e.g., hip replacement due to osteoarthritis, rheumatoid arthritis, osteonecrosis, fractures). |

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

• This is not applicable as it's not an AI/ML study. The "test set" here refers to mechanical and cadaveric testing.
• Mechanical Testing: The document mentions "worst case size for testing" for fatigue tests, implying specific sample selections rather than a broad 'test set' in the data sense.
• Cadaver Lab: Used for "validation demonstrating the design features," implying a limited number of cadavers for physical fit and design assessment.
• Data Provenance: Not explicitly stated as "data provenance" but the clinical literature review would draw from existing medical literature, likely international. Cadaver labs and mechanical testing are typically conducted in-house or by contract labs.

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

• Not applicable in the context of AI/ML. The "ground truth" for a medical device like this is established through engineering principles, adherence to recognized standards, and clinical experience of surgeons.
• The "experts" involved would be biomechanical engineers, materials scientists, regulatory specialists, and orthopedic surgeons (for cadaver lab validation and clinical input) but their number or specific qualifications are not detailed in this summary.

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

• Not applicable as it's not an AI/ML study involving expert consensus on data. The "adjudication" in this context would be regulatory review by the FDA.

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:

• No, an MRMC comparative effectiveness study was not done, as this is a physical medical device, not an AI/ML algorithm for image interpretation or diagnosis.

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

• Not applicable. This is a physical implant, not an algorithm.

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

• The closest equivalent to "ground truth" for this device would be established engineering principles, international performance standards for hip implants (e.g., EN ISO 21535), and clinical practice guidelines, validated through mechanical testing and cadaveric studies. The objective is to demonstrate safety and effectiveness for its intended use, primarily through mechanical integrity and functional fit.

8. The sample size for the training set:

• Not applicable as this is a physical medical device, not an AI/ML algorithm. There is no "training set" in this context. The design of the device would be informed by biomechanical principles, prior device designs, and clinical experience.

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

• Not applicable.

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