Objective
Lung cancer is one of the most common cancers with the highest mortality rate both in Europe and Worldwide. In 2012, 449,000 new cases were diagnosed with 388,000 deaths recorded in Europe alone. The reason for the 86% mortality rate is that most lung cancers are detected only after clinical symptoms are prevalent, by which time the cancer is in a late stage. Early detection using Chest Computed Tomography (CT) can lead to markedly improved outcomes, as shown recently by the NLST lung cancer screening trial in USA which has achieved a ground-breaking, 20% mortality reduction. However there is currently no solution that allows screening and management of suspected lung cancer patients in an economically sustainable manner.
Optellum has developed a novel technology, Deep Learnt Biomarker (DLB). It is a software application that will allow radiologist to make a more accurate diagnosis by providing additional information extracted from the same CT, already acquired anyway as standard of care. It is based on machine learning algorithms applied to large databases of CTs with known ground-truth diagnosis, which learns patterns not obvious to a human eye. Our product will a) improve early diagnosis of lung cancer, b) save EUR799M p.a. in unnecessary costs to EU health providers c) enable European Union to become the leader in lung cancer screening.
Optellum was founded to commercialize machine learning technologies that will transform radiology by learning novel biomarkers from medical image databases. During this Phase 1 SME project, we will investigate the commercial feasibility of this first application and will develop a detailed business plan, with a focus on a roadmap to navigate the complex regulatory and health economics environment.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- medical and health sciences clinical medicine oncology lung cancer
- medical and health sciences clinical medicine radiology
- social sciences economics and business economics health economics
- natural sciences computer and information sciences artificial intelligence machine learning
- natural sciences computer and information sciences software software applications
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.3.1. - SOCIETAL CHALLENGES - Health, demographic change and well-being
MAIN PROGRAMME
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H2020-EU.3.1.3. - Treating and managing disease
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
SME-1 - SME instrument phase 1
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-SMEInst-2014-2015
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
OX1 1BY OXFORD
United Kingdom
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.