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A multimodal AI-based toolbox and an interoperable health imaging repository for the empowerment of imaging analysis related to the diagnosis, prediction and follow-up of cancer

Project description

DEENESFRITPL

Improving cancer diagnosis and prediction with artificial intelligence

Although artificial intelligence (AI) and machine learning (ML) provide unprecedented opportunities for improved cancer detection, various technical challenges as well as a lack of data availability hamper their utilisation. The EU-funded INCISIVE project aims to develop a toolbox for enhancing the accuracy, specificity and sensitivity of existing cancer imaging methods. The idea is to generate a pan-European repository of medical images that can be used for ML-based training for various types of cancer. The project's deliverables will assist the accurate prediction of tumour spread, evolution and relapse, in addition to helping stratify patients.

Objective

The increasing amount and availability of collected data (cancer imaging) and the development of novel technological tools based on Artificial Intelligence (AI) and Machine Learning (ML), provide unprecedented opportunities for better cancer detection and classification, image optimization, radiation reduction, and clinical workflow enhancement. The INCISIVE project aims to address three major open challenges in order to explore the full potential of AI solutions in cancer imaging: (1) AI challenges unique to medical imaging, (2) Image labelling and annotation and (3) Data availability and sharing. In order to do that INCISIVE plans to develop and validate: (1) an AI-based toolbox that enhances the accuracy, specificity, sensitivity, interpretability and cost-effectiveness of existing cancer imaging methods, (2) an automated-ML based annotation mechanism to rapidly produce training data for machine learning research and (3) a pan-European repository federated repository of medical images, that will enable the secure donation and sharing of data in compliance with ethical, legal and privacy demands, increasing accessibility to datasets and enabling experimentation of AI-based solutions.
The INCISIVE models and analytics will utilize various cancer imaging scans, biological data and EHRs, and will be trained with 1 PB of available data provided by 8 partners within the project. INCISIVE solution will be investigated in four validation studies for Breast, Prostate, Colorectal and Lung Cancer, taking place in 8 pilot sites, from 5 countries (Cyprus, Greece, Italy, Serbia and Spain), with participation of at least 2,600 patients and a total duration of 1.5 year. INCISIVE moves beyond the state of the art, by improving sensitivity and specificity of lower cost scanning methods, accurately predicting the tumor spread, evolution and relapse, enhancing interpretability of results and “democratizing” imaging data.

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Call for proposal

H2020-SC1-FA-DTS-2018-2020

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Sub call

H2020-SC1-FA-DTS-2019-1

Funding Scheme

RIA - Research and Innovation action

Coordinator

MAGGIOLI SPA
Net EU contribution
€ 702 500,00
Address
Via del carpino 8
47822 Santarcangelo di romagna
Italy

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Region
Nord-Est Emilia-Romagna Rimini
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
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Other funding
€ 0,00

Participants (28)

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EC signature date 28 May 2020
Last update: 21 March 2023

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Permalink: https://cordis.europa.eu/project/id/952179

European Union, 2023