Digital twins, artificial intelligence, and machine learning technology to identify a real personalized motion axis of the tibiotalar joint for robotics in total ankle arthroplasty.

PURPOSE

Axial alignment of the talar implant in total ankle arthroplasty remains a major issue, since the real axis of motion of each patient is impossible to determine with usual techniques. Further knowledge regarding individual axis of motion of the ankle is therefore needed.

MATERIAL AND METHODS

Therefore, digital twins, artificial intelligence, and machine learning technology were used to identify a real personalized motion axis of the tibiotalar joint. Three-dimensional (3D) models of distal extremities were generated using computed tomography data of normal patients. Digital twins were used to reproduce the mobility of the ankles, and the real ankle of the patients was matched to the digital twin with machine learning technology.

RESULTS

The results showed that a personalized axis can be obtained for each patient. When the origin of the axis is the centre of mass of the talus, this axis can be represented in a geodesic system. The mean value of the axis is a line passing in first approximation through the centre of the sphere (with a variation of 3 mm from the centre of the mass of the talus) and through a point with the coordinates 91.6° west and 7.4° north (range 84° to 98° west; - 2° to 12° north). This study improves the understanding of the axis of the ankle, as well as its relationship to the possibility to use the geodesic system for robotic in ankle arthroplasty.

CONCLUSION

The consideration of a personalized axis of the ankle might be helpful for better understanding of ankle surgery and particularly total ankle arthroplasty.