Lymphoedema

Lymphoedema is the fluid retention in the interstitial space due to a compromised lymphatic system and this fluid homeostatic imbalance typically affects the limbs of patients. This work focuses on a data-driven modelling approach to find better ways to diagnose this disorder.

We developed a 3D camera-based assessment of lymphoedema which allowed us to identify localised swelling patterns for patients with unilateral lymphoedema. Furthermore, we quantified the variability of sub-garment pressures across scanned arms and questioned the validity of the Laplace assumptions for real arm shapes through a computational modelling approach. Part of this work was funded by the British Lymphology Society (BLS).

However, beyond geometric considerations we should also consider skin material properties for optimal patient treatment strategies. Therefore, a framework is currently being developed that seeks to mechanically characterise skin through suction experiments combined with computational modelling and machine learning approaches. Validation of this framework is sought through the development of multi-layered hydrogel-based skin phantoms that are tested in our lab. The modelling and lab work is complemented by non-invasive measurements on volunteers and patients in collaboration with Lymphoedema Wales Clinical Network (LWCN) and is currently funded by the EPSRC Impact Accelerator Account. This work is currently pursued by Casey Smith (2023-2024).