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Completed H2020 European Commission

Intelligent Total Body Scanner for Early Detection of Melanoma

€12.04M EUR

Funder European Commission
Recipient Organization Universitat de Girona
Country Spain
Start Date Apr 01, 2021
End Date Mar 31, 2025
Duration 1,460 days
Number of Grantees 22
Roles Third Party; Participant; Coordinator
Data Source European Commission
Grant ID 965221
Grant Description

Melanoma is one of the most aggressive cancers that can be discovered at an early stage, and it is responsible for 60% of lethal skin neoplasia.

Its incidence has been increasing in white population and could become a public health challenge because of an increase in life expectancy of the elderly population.

Total body skin examination, the primary screening mechanism for melanoma, checks each pigmented skin lesion individually in search of typical melanoma signs.

This can be a very time consuming technique for patients with atypical mole syndrome or a large number of naevi.iToBoS aims at developing an AI diagnostic platform for early detection of melanoma.

The platform includes a novel total body scanner and a Computer Aided Diagnostics (CAD) tool to integrate various data sources such as medical records, genomics data and in vivo imaging. This approach will lead to a highly patient-tailored, early diagnosis of melanoma.

The project will develop and validate an AI cognitive assistant tool to empower healthcare practitioners, offering a risk assessment for every mole.

Beyond integrating all available information about the patient to personalise the diagnostic, it will provide methods for visualising, explaining and interpreting AI models, thus overcoming the “black box” nature of current AI-enabled CAD systems, and providing dermatologists with valuable information for their clinical practice.

The new total body scanner will be based on an existing prototype developed by 3 of the project partners, but powered with high-resolution cameras equipped with liquid lenses.

These novel lenses, based on two immiscible fluids of different refractive index, will allow achieving unprecedented image quality of the whole body.

The integration of such images with all available patient data using machine learning will lead to a new dermoscopic diagnostic tool providing prompt, reliable and highly personalised diagnostics for optimal judgement in clinical practice.

All Grantees

Robert Bosch Espana Slu; The University of Queensland; Universita Degli Studi Di Trieste; Barco Nv; Trilateral Research Limited; Universitat de Girona; Ethnicon Metsovion Polytechnion; Fraunhofer Gesellschaft Zur Forderung Der Angewandten Forschung Ev; Isahit; V7 Ltd; Fundacio de Recerca Clinic Barcelona-Institut D Investigacions Biomediques August Pi I Sunyer; Hun-Ren Szamitastechnikai Es Automatizalasi Kutatointezet; Coronis Computing Sl; Hospital Clinic de Barcelona; Robert Bosch Espana Fabrica Madrid Sa; Canfield Scientific Gmbh; Melanoma Patient Network Europe; Torus Actions; Ibm Israel - Science and Technology Ltd; Optotune Switzerland Ag; Ricoh Spain It Services Slu; Gottfried Wilhelm Leibniz Universitaet Hannover

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