Background
The majority of leg ulcers develop due to poor circulation in the veins (venous leg ulcers). These ulcers can be painful, itchy and give off an unpleasant smell. Patients are less able to look after themselves, and may be less willing to go out. This affects 560 thousand adults per year in the UK.
Before a wound appears, there are skin changes that show someone has increased pressure in their veins caused by weak or damaged valves. If these skin changes are spotted early, circulation can be improved before a leg ulcer begins. However, these visual skin changes are less readily observed in people with dark skin tones. Therefore, doctors and nurses rely on the patient’s history and symptoms to inform their care and referral to specialist vascular services. However, this relies on clinical experience and exposure to patients with a range of skin tones to inform their decisions.
Novelty & Importance
Information obtained from machine learning within this project will develop a novel tool to support doctors and nurses decision-making. This is especially important for patients where skin changes are less readily observed and patient history and symptoms are relied on. The development of this tool could therefore improve the early recognition of venous damage across skin tones and reduce health inequity.
Aims & Objectives
The project aim is to determine the contribution of risk factors and symptoms that pre-date the development of a venous leg ulcer in people across skin tones. It is envisaged this knowledge will result in earlier referral to vascular specialists and the early commencement of preventative measures to stop venous leg ulcers from occurring.

