I am leading a feasibility study called MoleGazer which aims to help people who have lots of moles and are at high risk of developing skin cancer.
Early diagnosis of skin cancer (melanoma) is essential to improve survival. Melanoma frequently develops from existing moles on the skin. Current practice relies on expert dermatologists being able to successfully identify any new or changing moles in individuals who may have many (> 60) irregularly shaped moles.
For people with multiple moles, total body photography (TBP – high-quality images of the entire skin) can be used to track and monitor these moles over time so that melanoma can be detected as early as possible. However, TBP is currently only used as a visual guide when diagnosing melanoma, requiring a dermatologist to visually inspect each mole sequentially. This process is challenging, time-consuming and inefficient. Artificial intelligence (AI) is ideally suited to automate this process. Comparing baseline TBP images to newly acquired photographs, AI techniques can be used to accurately identify and highlight to the dermatologist which moles have changed, and potentially distinguish harmless moles from cancerous changes.
Astrophysicists face a similar problem when they map the night sky to detect new events, such as exploding stars. Using AI, based on two or more images, astrophysicists are able to detect new events and accurately predict how they will appear subsequently.
This project called MoleGazer, is a collaboration with astrophysicists aiming to apply AI methods that are currently used for astronomical sky surveys, to TBP images. The MoleGazer algorithm, developed here, will automatically identify the appearance of new moles and characterise changes in existing ones, when new TBP images are taken. However, to optimise this algorithm requires a training sample of TBP images taken at multiple time points, and there are no existing datasets of TBP images that are publicly available. We therefore aim to invite a) high-risk patients attending skin cancer screening clinics to attend for sequential three-monthly TBP imaging and clinical assessment and b) any patient who undergoes TBP as standard care to share their images so that we can develop the MoleGazer algorithm.
Our ultimate goal is for the MoleGazer algorithm to ‘map moles’ over a patient’s lifetime to detect changes, with the eventual aim of using this technique to detect melanoma as early as possible.
Date required
We are recruiting to this study until August 2023.
Organisation
Dermatology department, Oxford University Hospitals NHS Foundation Trust.
Contact
If you are interested in participating in this trial and want more information please contact us directly.
Melanie Westmoreland (Dermatology Research Nurse Lead), email: melanie.westmoreland@ouh.nhs.uk, or
telephone 01865 228252
Dr Rubeta Matin (Chief Investigator), email rubeta.matin@ouh.nhs.uk