Melanoma (skin cancer) frequently develops from existing moles on the skin. Current practice relies on expert dermatologists being able to successfully identify new/changing moles in individuals with multiple moles. Total body photography (TBP-high-quality images of the entire skin) can track and monitor moles over time to detect melanoma. However, TBP is currently used as a visual guide when diagnosing melanoma, requiring visual inspection of 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 changing moles, 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 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 at Oxford University Hospitals NHS Foundation Trust, will automatically identify the appearance of new moles and characterise changes in existing ones, when new TBP images are taken. To optimise this MoleGazer algorithm TBP images will be taken at multiple time-points, as there are no existing datasets of TBP images that are publicly available. The investigators invite a) high-risk patients attending skin cancer screening clinics to attend sequential three-monthly TBP imaging and clinical assessment and b) any patient who undergoes TBP as standard care to share images so that the investigators can develop the MoleGazer algorithm. The ultimate goal is for the MoleGazer algorithm to 'map moles' over a patient's lifetime to detect changes, with the eventual aim to detect melanoma as early as possible.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Functional algorithm to map naevi sequentially
Timeframe: 3 years
Number of TBP images in database
Timeframe: 3 years