To develop a computer vision tool that can automatically confirm the diagnosis and assessment ...
Finding Endometriosis using Machine Learning is a Horizon2020 project with focus entirely on improving diagnosis, prevention and care in endometriosis. The FEMaLe project will build bridges across disciplines and sectors to translate genetic and epidemiological knowledge into clinical tools that support decision-making in terms of diagnosis and care aimed at both general practice and highly specialized endometriosis clinics – all via machine learning and artificial intelligence.
Increasing education and disease awareness for patients, public & healthcare providers will increase timely & accurate diagnosis and treatment, and allow for new advancements.
The research activities such as data collecting & analysis and identifying patterns and models within FEMaLE will have in view the scientific utility and value.
One of the main FEMaLe project’s goals is to generate knowledge.
The publications created by the project’s researchers will be published here soon.
FEMale Video Explainer
Presenting data scientists Palle Duun Rohde and Peter Loof Møller from the Genomic Medicine ...
WP5 is creating a large prospective database using the Lucy mobile health application (Lucy ...
To develop a platform for discovery and replication of specific combinations of SNP genotypes ...
Presenting Mette Nyegaard who is professor in Genetics and Personalised Medicine and Head og ...