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MOLEGAZER

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Mole Gazer Proof of concept study to improve early detection of melanoma using...

Mole Gazer Proof of concept study to improve early detection of melanoma using time series analyses of evolution of naevi 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 inte... see more

31/10/2022

SOUTHAMPTON

150K€

Project Budget: 150K€

Project leader

UNIVERSITY OF SOUTHAMPTON No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

PARTICIPATION DEPralty Sin fecha límite de participación.

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Financing granted El organismo H2020 notifico la concesión del proyecto The day 2022-10-31

ERC-2019-POC: ERC Proof of Concept Grant Scope:Eligible Countries

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Cerrada does 6 years

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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2 Participantes

SOUTHAMPTON

150.00K€ | Leader

LLAMAS

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Project duration: 27 months Date Start: 2020-07-01
End date: 2022-10-31

Project leader

UNIVERSITY OF SOUTHAMPTON No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Project Budget 150K€

participation deadline Sin fecha límite de participación.

Project description 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.

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