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The Force Medical IM Nail is intended for tibiotalocalcaneal (TTC) arthrodesis (fusion) and to provide stabilization of the hindfoot and ankle including the transverse tarsal joints coupling the mid-foot to the hindfoot.

Indications for use include:

• Posttraumatic and primary Arthrosis
• Neuromuscular deformity
• Revision of Failed Ankle Arthrodesis
• Failed Total Ankle Replacement
• Avascular Necrosis of the Talus (requiring tibiocalcaneal arthrodesis)
• A Neuroarthropathy (Charcot)
• Rheumatoid Arthritis with severe deformity
• Osteoarthritis
• Pseudarthrosis

The Force Medical IM Nail System is an angled (left & right) and straight (left & right due to hole orientation), cannulated, intramedullary nail in multiple diameters(8) and in multiple lengths(3) manufactured from High Strength 6-4 Titanium Alloy to ASTM F136. The nail features proximal, distal, and midshaft angled hole(s) to receive Alloyed Titanium transverse locking 4.5mm Cortical and 4.5/6.5 Transition Cortical - Cancellous Bone Screws (in various lengths). Endcaps (in 0+ and 6 lengths in 4 diameters) in the Titanium Alloy are available for nail end bottom screw locking and IM Nail length extension. A Titanium Alloy Compression Washer and Screw (in 5 lengths) is also available for joint compression(s) if distal talocalcaneal fusion is required. All alloyed titanium components have a Type II anodize surface treatment and are anodize color coded for left/right versions. A full set of guide/placement instrumentation is available for accurate placement and alignment of the bone screws within the nail when used in conjuction with x-ray fluoroscopy.

Here's an analysis of the provided text regarding the acceptance criteria and supporting studies for the Force Medical IM Nail System:

Based on the provided 510(k) summary, the device is an intramedullary nail for orthopedic use, not an AI/ML powered medical device. Therefore, typical performance metrics such as sensitivity, specificity, or AUC, and ground truth establishment methods relevant to AI/ML algorithms, are not applicable.

Instead, the acceptance criteria and the study proving it meets the criteria for this type of medical device revolve around substantial equivalence to predicate devices. This means demonstrating that the new device has the same intended use, technological characteristics, and safety and effectiveness profiles as legally marketed predicate devices.

1. Table of Acceptance Criteria and Reported Device Performance

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

• For this type of device (intramedullary nail), there isn't a "test set" in the sense of a dataset for an AI/ML algorithm. Instead, the "testing" involves demonstrating that the device's design, materials, and manufacturing processes lead to a product that is substantially equivalent to already cleared devices. This often involves:
  • Mechanical testing: To ensure the device meets required strength, fatigue, and other biomechanical properties.
  • Biocompatibility testing: To ensure the materials are safe for implantation.
  • Sterilization validation: To ensure the device can be properly sterilized.
• The provided document does not specify sample sizes for such tests or data provenance (e.g., country of origin, retrospective/prospective). This information would typically be detailed in the full 510(k) submission, but is summarized here. The summary focuses on the conclusion of substantial equivalence rather than the raw data or detailed methodologies of the tests performed.

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

• This question is not applicable as there is no "ground truth" to be established by experts in the context of an AI/ML algorithm evaluation. The "ground truth" for a mechanical device is established through engineering standards, material specifications, and regulatory requirements (e.g., ASTM standards for titanium alloy, biocompatibility guidelines).
• The evaluation involves engineers and scientists performing the specified tests, and regulatory bodies (like the FDA) reviewing the results against established standards and predicate device data.

4. Adjudication method for the test set:

• This question is not applicable as there is no "test set adjudication" process for this type of medical device in the way it applies to AI/ML output interpretation. The "adjudication" is essentially the FDA's review process of the submitted technical data and comparison to predicate devices, ensuring compliance with regulatory requirements.

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:

• This question is not applicable as this is an orthopedic implant, not an AI-assisted diagnostic tool. No human "readers" are involved in interpreting output from this device in a clinical diagnostic setting where MRMC studies would be relevant.

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

• This question is not applicable as this is not an AI/ML algorithm. The device functions as a standalone mechanical implant.

7. The type of ground truth used:

• As mentioned, the concept of "ground truth" for an AI/ML algorithm does not apply directly. For this device, the "ground truth" for demonstrating safety and effectiveness refers to:
  • Engineering standards: e.g., ASTM F136 for titanium alloy, various mechanical testing standards.
  • Biocompatibility standards: ISO 10993 for biological evaluation of medical devices.
  • Sterilization standards: ISO 11135 or ISO 11137 for sterilization of health care products.
  • Performance criteria based on predicate devices: The established safety and effectiveness profile of the T2 Ankle Arthrodesis Nail, Phoenix Nail Systems, and Valor Ankle Fusion Nail serve as a benchmark for substantial equivalence.

8. The sample size for the training set:

• This question is not applicable as this is not an AI/ML device that requires a training set.

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

• This question is not applicable as this is not an AI/ML device that requires a training set and associated ground truth.

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