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The INBONE® Total Ankle is intended to give a patient limited mobility by reducing pain, restoring alignment and replacing the flexion and extension movement in the ankle joint.

The INBONE® Total Ankle is indicated for patients with ankle joints damaged by severe rheumatoid, post-traumatic, or degenerative arthritis.

The INBONE® Total Ankle is additionally indicated for patients with a failed previous ankle surgery.

CAUTION: The ankle prosthesis is intended for cement use only.

The INBONE® implant is a total ankle replacement (TAR) that is made of several specific components. A key concept of the INBONE® device is its modularity. Based on patient anatomy, a number of component sizes can be optioned for best fit. These modular components include the tibial stem components, the tibial tray, the poly bearing, the talar dome, and the talar stem. Subject of this submission is an expansion of labeling while Wright is also notifying FDA of additional implant sizes.

The provided document describes a 510(k) summary for the INBONE® Total Ankle System, focusing on an expansion of labeling and additional implant sizes. It details the device's technical characteristics and the non-clinical evidence used to demonstrate substantial equivalence to predicate devices. However, the document does not describe acceptance criteria or a study proving the device meets them in the context of typical AI/ML medical device performance evaluations (e.g., sensitivity, specificity, clinical outcome measures).

Instead, the submission refers to the following types of evidence:

• Performance fatigue testing: for the extended total tibial stem length.
• Mechanical analysis: for new tibial stem diameter and shorter total tibial stem length, and new poly bearings.
• Analyses of computed tomography scans: used to support larger sized stems.

The purpose of these tests is to demonstrate that the new designs and sizes are "substantially equivalent" to previously cleared predicate devices, meaning they perform at least as well and do not raise new questions of safety or effectiveness. This is a common approach for device modifications under 510(k) regulations for mechanical devices.

Therefore, many of the specific questions about acceptance criteria, test sets, ground truth establishment, expert involvement, and comparative effectiveness studies as they relate to AI/ML device performance are not applicable to the information provided in this document.

Here's an attempt to answer the questions based only on the provided text, while acknowledging its limitations regarding typical AI/ML device evaluation criteria:

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

The document does not explicitly state quantitative "acceptance criteria" (e.g., minimum tensile strength, maximum wear rate) or "reported device performance" in a numerical table. Instead, it refers to establishing "substantial equivalence" based on various analyses. The implicit acceptance criterion is that the modified device components perform at least as well as, and do not represent a new worst-case compared to, the predicate devices.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size: Not specified in the document for any of the analyses (fatigue testing, mechanical analysis, or CT scan analysis).
• Data Provenance: Not specified. It's likely that the mechanical and fatigue testing was performed in a lab setting, possibly by the manufacturer. The source of the CT scans for analysis is not mentioned.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable. The ground truth for mechanical device performance is typically based on engineering standards, material properties, and established testing methodologies, rather than human expert consensus on diagnostic interpretations. The CT scan analysis was used to support stem sizing, not to establish a diagnostic ground truth involving expert readers.

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

Not applicable. This concept is relevant for studies involving human interpretation or subjective assessments, which is not described here for the device's performance evaluation.

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 is not a software/AI device, and no MRMC study is mentioned. The submission explicitly states "Substantial Equivalence - Clinical Evidence N/A".

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

Not applicable. This is not a software/AI device. The performance evaluations described are for the physical components of the ankle prosthesis.

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

The "ground truth" for the non-clinical evidence provided would be:

• Mechanical and Fatigue Testing: Engineering specifications, material properties, and established biomechanical standards (e.g., ISO, ASTM standards for implants).
• CT Scan Analysis: Anatomical measurements derived from imaging, potentially related to anthropological data or surgical planning guidelines for optimal fit.

8. The sample size for the training set

Not applicable. There is no mention of a "training set" as this is not an AI/ML device.

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

Not applicable. There is no mention of a "training set" or its ground truth establishment.

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