Marco Viceconti's KEYNOTE seminar "From Solid Mechanics to In Silico Trials"

Marco Viceconti Jan_12_2023.pdf (pdf 174 kB)

Abstract. The frailty associated with ageing manifests in many ways, but the proximal femur fracture is particularly critical. Fragile elders tend, for many reasons, to fall more frequently; this, combined with the progressive loss of bone mass, sometimes produces the so-called fragility fractures, bone fractures caused by low energy impacts. Of all fragility fractures, the proximal femur fracture (PFF) is the most debilitating: it always requires surgery, kills around 25% of patients due to related complications within a year, and leaves some permanent functional disabilities in another third. But until the fracture occurs, the subject is fine and has no evident symptoms. Thus, predicting who is at risk of PFF is a clinical holy grail. A body falling, impacting the ground, and a bone fracturing because of the impact, sounds like an event where biomechanics plays a bigger role than biology. This has driven various research groups to explore the development of patient-specific biophysics models that could predict the risk of PFF in a patient. Over the years, these digital twins in healthcare became quite accurate, to the point where they are now commercialised as clinical decision support systems. In this seminar, we present the journey to develop and validate one of such tools and how now that digital twin technology is being adapted to become a full-blown In Silico Trial to test the efficacy of new treatments.