In 2019, the University of Tartu, Rīga Stradiņš University, Lithuanian University of Health Sciences, and the Norwegian Cancer Registry began the project “Towards elimination of cervical cancer: intelligent and personalized solutions for cancer screening” (2020-2023).


Main objective

The project’s main objective is to develop improved and personalized cancer screening methods within a sustainable health care system. The developed methods will integrate knowledge of biological disease mechanisms and available data from national population-based health registries, health care provision data, surveys, and Estonian genome bank to develop, validate, and determine the cost-effectiveness of, specific artificial intelligence technology for the purpose of preventive medicine in cervical cancer. The foundation of this project relies on combining population based multi-faceted individual data (HPV-status, health and reproductive behavior information; individual histories of cervical cancer screening; genetic data) with the advantages of high-performance computing and analytics (AI) to leverage existing knowledge and experience for transforming cancer screening systems towards higher inclusiveness as well as making them increasingly flexible, scalable and sustainable.

Similar projects

The CBIG-SCREEN project  aims to reduce inequality by improving the offer of cervical cancer screening to vulnerable and underserved groups. Though Cervical Cancer Screening (CCS) programmes drastically reduce cervical cancer mortality, they remain largely inaccessible and underused by subpopulations of vulnerable women, exacerbating inequality. CBIG-SCREEN will develop sound evidence around barriers to cervical cancer screening and pilot interventions with strategic outcomes to inform policy making to be updated after the decision is made on the categorisation of vulnerable groups. Last but not the least, the consortium will generate policies, programmes, communications and other services to meet the needs, and advise policymakers to develop recommendations.

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