Project description DEENESFRITPL Astronomy-based AI for melanoma prediction Sequential monitoring of skin moles by a dermatologist constitutes the gold standard method for early melanoma detection. However, this approach is time-consuming and inefficient. Scientists of the EU-funded MOLEGAZER project propose to develop an automated total-body photography method for monitoring skin moles that exploits artificial intelligence algorithms used in astronomy to monitor the night sky and predict the nature and outcome of various events. The proposed approach will utilise skin images to automatically detect changes in moles and enable the robust prediction of the evolution of skin lesions, paving the way for the early detection of melanoma. Show the project objective Hide the project objective Objective Early detection of melanoma improves survival. Individuals with multiple naevi (moles) are at an increased risk of developing melanoma, but sequential monitoring by dermatologists is time-consuming and inefficient. Artificial intelligence (AI) methods can potentially diagnose melanoma from single time-point lesion images, but a more clinically-relevant question is whether melanoma can be detected early, based on automated detection of changes in naevi using total body photography (TBP; high-resolution standardised images of body-parts).Cutting edge astronomical surveys use sequential images to monitor the night sky. With state-of-the-art AI techniques, these surveys identify and track subtle changes, robustly classifying the nature and prognosis of each event from just three images. Both astronomy and dermatology therefore face similar challenges: robustly predicting the evolution of sources from sparsely-sampled images. With this motivation we propose an innovative solution: adapting AI algorithms, developed in astronomy and the ERC SPCND project, for use in the automated detection, characterisation and monitoring of skin lesions. With a wealth of experience tackling this problem in astronomy, this proof-of-concept project will characterise the properties and evolutionary path of naevi in preparation for the next stage: the early detection of melanoma. Fields of science natural sciencescomputer and information sciencesartificial intelligencemedical and health sciencesclinical medicinedermatologymedical and health sciencesclinical medicineoncologyskin cancermelanomanatural sciencesphysical sciencesastronomy Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Topic(s) ERC-2019-POC - ERC Proof of Concept Grant Call for proposal ERC-2019-PoC See other projects for this call Funding Scheme ERC-POC - Proof of Concept Grant Coordinator UNIVERSITY OF SOUTHAMPTON Net EU contribution € 150 000,00 Address Highfield SO17 1BJ Southampton United Kingdom See on map Region South East (England) Hampshire and Isle of Wight Southampton Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window EU contribution No data Beneficiaries (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all UNIVERSITY OF SOUTHAMPTON United Kingdom Net EU contribution € 150 000,00 Address Highfield SO17 1BJ Southampton See on map Region South East (England) Hampshire and Isle of Wight Southampton Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window EU contribution No data
