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1. Non-inflammatory degenerative joint disease including osteoarthritis, traumatic arthritis, 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.
6. Cemented and uncemented applications.

The Provident II Hip Stem is a femoral prosthesis intended to replace a hip joint. The subject stem is a line extension of the Provident II Hip System and is fully compatible with existing femoral heads and liners. The stem is composed of titanium alloy, and is coated with commercially pure titanium.

This appears to be a 510(k) premarket notification for a medical device, specifically the Provident II Hip Stems, and not a study describing acceptance criteria and device performance in the context of an AI/ML medical device. Therefore, I cannot extract the requested information such as test set sample size, ground truth establishment, or clinical study details.

The document discusses:

• Device: Provident II Hip Stems
• Purpose: Clearance for additional implants (line extension)
• Indications for Use: Standard indications for hip arthroplasty (degenerative joint disease, rheumatoid arthritis, fracture treatment, revision, etc.)
• Technological Characteristics: States it has the same characteristics as predicate devices.
• Performance Testing: Mentions mechanical testing (Stem Fatigue, Neck Fatigue) conducted in accordance with ISO standards and FDA guidance for femoral stem prostheses. This is physical mechanical testing of the implant itself, not a clinical study involving patients or AI performance.
• Substantial Equivalence: Basis is stated to be similar technological characteristics, performance, design, and intended use to predicate devices.

There is no mention of:

• Any AI/ML component.
• Acceptance criteria for an AI algorithm.
• A "study that proves the device meets the acceptance criteria" in the context of AI/ML performance.
• Test sets, training sets, ground truth, experts, or adjudication methods related to diagnostic or analytical performance.
• MRMC studies or standalone algorithm performance.

Therefore, I cannot provide the requested table or answer the specific questions related to AI/ML device performance. The provided text describes a traditional medical device clearance based on substantial equivalence and mechanical performance testing, not an AI efficacy study.

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