Project description
AI to analyse and detect ciliary beat defects
Rhythmically beating ciliated cells play a crucial role in various physiological functions, particularly in the airways and reproductive system. Defects in ciliary beating can lead to debilitating diseases, such as chronic obstructive pulmonary disease, which is currently incurable. However, the complexity of ciliary beat patterns makes large-scale analysis challenging. The ERC-funded Ai4Cilia project aims to develop an automated, microfluidic, AI-powered assay to detect and classify ciliary beat abnormalities. By integrating microfluidic technology and AI, the project will offer a solution for detecting and classifying ciliary beat defects, providing significant benefits for drug discovery, disease screening, and biomedicine.
Objective
In Ai4Cilia (“Artificial intelligence for Cilia”), we will develop an automated microfluidic AI-powered assay of ciliated cells to detect and classify ciliary beat abnormalities for drug discovery, biomedicine, and disease screening. Rhythmically beating ciliated cells perform important physiological functions in the airways and the reproductive system. Defects of ciliary beat contribute to debilitating diseases, such as to-date uncurable chronic obstructive pulmonary disease (COPD), and underdiagnosed causes of infertility. In the context of our ERC StG MecCOPD, we recently showed that standard video-microscopy recordings of ciliary beat can be used to extract quantitative metrics that clearly identify disease-specific ciliary dysfunction. Therefore, since ciliary beat can be observed in vitro and in minimally invasive biopsies, it could provide a much-needed readout for drug development and improved diagnosis. However, since ciliary beat patterns are highly complex, it takes specialists to analyze them and hence this information is rarely exploited in praxis. To establish ciliary beat as a common readout, we developed a microfluidic platform that standardizes data acquisition from ciliated cells and uses AI for detecting and classifying ciliary beat defects. To bring this approach to the next level and towards market entry, we will address three key objectives in Ai4Cilia to demonstrate (1) hardware feasibility by validating human ciliated cell capture and data acquisition in our microfluidic chip; (2) software feasibility by training our AI algorithm to detect disease-specific ciliary defects and validate sensitivity and specificity of our assay; and (3) business feasibility by assessing customer needs in biomedical and clinical research to define the most efficient go-to-market strategy. We believe this strategy will make ciliary beat a highly sensitive and robust readout for respiratory and reproductive (dys)function in biomedical and clinical research.
Fields of science (EuroSciVoc)
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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.
- natural sciences computer and information sciences software
- social sciences other social sciences development studies social developments
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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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HORIZON.1.1 - European Research Council (ERC)
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Topic(s)
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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.
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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.
HORIZON-ERC-POC - HORIZON ERC Proof of Concept Grants
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2024-POC
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85764 Neuherberg
Germany
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