Digital Twins of the Living Knee: From Measurements to Model

Abstract

Modern medicine has dramatically improved the lives of many. In orthopaedics, robotic surgery has given clinicians superior accuracy when performing interventions over conventional methods. Nevertheless, while these and many other methods are available to ensure treatments are performed successfully, far fewer methods exist to predict the proper treatment option for a given person. Clinicians are forced to categorize individuals, choosing the best treatment on “average.” However, many individuals differ significantly from the “average” person, for which many of these treatments are designed. Going forward, a method of testing, evaluating, and predicting different treatment options' short- and long-term effects on an individual is needed. Digital Twins have been proposed as one such method.

While Digital Twins have grown in popularity in recent years in many fields to understand and predict a range of phenomena, healthcare has been slow to adopt and create Digital Twins, as until recently, few methods existed to make measurements on living individuals to determine individual geometry and material properties accurately.

This work aims to determine what kinds of data should be captured in living individuals and how best to use this data to build computer models able to replicate their joint behavior. A set of template models and FEA manipulation algorithms are presented to improve the accuracy and reduce the time required to build models. Then, a set of works investigating the reproducibility and accuracy associated with different model calibration methods and data sources on model performance is presented. Lastly, ongoing work is introduced that uses lessons from all other sections to develop and validate Digital Twin models of two living individuals.

Overall, this work shows that currently available in vivo techniques for laxity measurement are sufficient to create a model. Yet, the accuracy of models can often hinge on the accuracy of the geometries, particularly ligament attachments. This work introduced methods to improve the speed and accuracy used to create geometries through the morphing of template geometries and using subject-specific kinematics to inform better predictions of ligament attachment sites. The processes and lessons learned can be used in future work to better model living individuals.