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Contact the study team using the details below to take part. If there are no contact details below please ask your doctor in the first instance.
Dr
Rubeta
Matin
rnhmatin@doctors.org.uk
Dr
Rubeta
Matin
rubeta.matin@ouh.nhs.uk
Dr
Rubeta
Matin
rubeta.matin@ouh.nhs.uk
Joanna
Searle
joanna.searle@ouh.nhs.uk
Benign neoplasmsMelanoma and other malignant neoplasms of skin
This information is provided directly by researchers, and we recognise that it isn't always easy to understand. We are working with researchers to improve the accessibility of this information. In some summaries, you may come across links to external websites. These websites will have more information to help you better understand the study.
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 here, 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. We 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 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 to detect melanoma as early as possible.
Start dates may differ between countries and research sites. The research team are responsible for keeping the information up-to-date.
The recruitment start and end dates are as follows:
Type: Imaging;Active Monitoring;
You can take part if:
You may not be able to take part if:
The participant may not enter the study if ANY of the following apply: ● Patient unable to consent ● Poor mobility / unable to hold recommended positions for standard TBP imaging ● Individuals who do not understand english In addition for Group A: ● Unable to attend for three-monthly study visits
Below are the locations for where you can take part in the trial. Please note that not all sites may be open.
Dr
Rubeta
Matin
rubeta.matin@ouh.nhs.uk
Dr
Rubeta
Matin
rnhmatin@doctors.org.uk
Joanna
Searle
joanna.searle@ouh.nhs.uk
Dr
Rubeta
Matin
rubeta.matin@ouh.nhs.uk
The study is sponsored by OXFORD UNIVERSITY HOSPITALS NHS FOUNDATION TRUST and funded by University of Portsmouth; European Research Council; .
Your feedback is important to us. It will help us improve the quality of the study information on this site. Please answer both questions.
Read full details
for Trial ID: CPMS 48060
You can print or share the study information with your GP/healthcare provider or contact the research team directly.
