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IMAGING FOR PATIENT BENEFIT IN ARTHRITIS

Project description

Imaging strategy tools for arthritis diagnoses and monitoring (IHI-AutoPiX)

Patients at risk of or diagnosed with arthritis are commonly evaluated using innovative imaging techniques to identify disease onset and track progression. However, analytical tools for accurate and appropriate evaluation of these medical images are lacking. Additionally, advanced imaging methods such as ultrasound are not widely accessible. The EU-funded AutoPiX project aims to address these challenges by developing tools that transform unstructured images into quantitative biomarkers through artificial intelligence (AI) and machine learning (ML). These biomarkers will undergo clinical validation for diagnosis, monitoring and prognosis, offering improved utility in arthritis care compared to traditional clinical/laboratory biomarkers. The project will also promote accessible imaging strategies such as self-acquired imaging, remote monitoring or robotic ultrasound to mitigate the shortage of qualified personnel.

Objective

Patients at risk or diagnosed with arthritis are constantly assessed by innovative imaging techniques to document the onset or progression of their disease. However, despite their impressive abundance and resolution, these images lack the analysis and interpretation tools necessary to deliver unbiased and precise diagnosis, monitoring and prognosis to the patients. Additionally, some key advanced imaging methodologies such as ultrasound are hardly accessible to most of patients, urging improvements of more accessible imaging methods.
The AutoPiX project is an ambitious international multi-stakeholder effort led jointly and synergistically by academic and industry partners to achieve breakthroughs in both the applicability and harnessing of imaging technologies for the benefit of patients by creating new powerful analysis and decision tools.
We will first generate tools to transform unstructured images into quantitative biomarkers using artificial intelligence (AI) and machine learning (ML) models, and validate them clinically for their diagnosis, monitoring and prognosis power. This will considerably increase the utility of imaging biomarkers for arthritis and bring them to the same level as laboratory biomarkers.
In parallel we will develop accessible imaging strategies such as remote monitoring and robotic-powered point-of-care ultrasound exams for patients to mitigate the often-observed shortage of qualified personnel in real world settings. To achieve this, we will improve the precision and interpretability of these methods and further validate them with clinical, molecular and histological analyses.
Our consortium is built on multi-disciplinarity and the constant synergistic interaction of all the actors of arthritis care: rheumatologists, radiologists, patients, researchers, regulators, industries and small- and medium sized enterprises (SMEs). On the long term, AutoPiX will create new clinically-validated methods to achieve a/ more precise, accessible and effective diagnosis, b/ shortened and better-tailored treatment paths and c/ improved treatment response assessments and outcome prediction for patients with arthritis.

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Topic(s)

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Funding Scheme

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HORIZON-JU-RIA - HORIZON JU Research and Innovation Actions

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Call for proposal

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(opens in new window) HORIZON-JU-IHI-2024-07-single-stage

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Coordinator

MEDIZINISCHE UNIVERSITAET WIEN
Net EU contribution

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.

€ 2 804 288,75
Address
SPITALGASSE 23
1090 Wien
Austria

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Region
Ostösterreich Wien Wien
Activity type
Higher or Secondary Education Establishments
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Total cost

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.

€ 3 304 288,75

Participants (15)

Partners (4)

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