FEMaLe in a nutshell
About the project
The challenges such as: increasingly ageing population, multiple chronic conditions, lack of coherent monitoring, collection and data usage to support clinical decisions and treatments, diagnostic delay, misdiagnosis or lack of a diagnosis altogether, have as a result that the European Healthcare systems are currently responsive, rather than preventive.
This will lead to facing many more patients suffering from chronic diseases, reducing quality of life and increasing healthcare expenditures.
The Finding Endometriosis using Machine Learning project (FEMaLe) will develop and demonstrate the Scalable Multi-Omics Platform that converts multi-omic person population datasets into a personalised predictive model to improve intervention along the continuum of care for people with endometriosis.
We will design, validate and implement a comprehensive model for the detection and management of people with endometriosis to facilitate shared decision making between the patient and the healthcare provider, enable the delivery of precision medicine, and drive new discoveries in endometriosis treatment to deliver novel therapies and improve quality of life for patients.
The FEMaLe project relies on participatory processes, advanced computer sciences, genetics, state-of-the-art technologies, and patient-shared data to deliver:
Mobile health app for people with endometriosis.
Three clinical decision support (CDS) tools for targeted healthcare providers.
Computer vision-based software tool for real-time augmented reality guided surgery of endometriosis.
Preventive responsive actions to people suffering from diseases, including endometriosis, will greatly optimize the quality of life and also reduce healthcare costs, e.g., through reduced number of surgeries, hospitalizations and rehabilitation programmes. By collecting patient-reported data on symptoms in a large random sample of people in the reproductive age, FEMaLE will get valid estimates on the extent and geographical distribution of debilitating pelvic pain in this group.
By linking this information with existing registry-based information on diagnoses of endometriosis, genetics bio-markers, healthcare use, other somatic and mental health as well as socio economic indicators, it will be possible both to get an estimate of the health related and social consequences of diagnostic delay, but also to develop a phenotype description of people with endometriosis to be used to achieve early diagnosis and treatment of endometriosis with pelvic pain. Imagine if endometriosis could be diagnosed and treated sooner to bring this burden down – not only for those affected by endometriosis, but also for society in general.
FEMaLE will combat the negative effects of the disease on patients, particularly the heavy impact on work, relationships, and the sex-lives of people with endometriosis, and challenges to healthcare providers in primary and secondary care. Ultimately, it will enable healthcare providers and patients to perform shared decision making to improve informed decisions, patient safety and personalized treatment regimes.
FEMaLE will stress equity, ethics, and empowerment through education in health literacy, and ensure that all patients, whether vulnerable or resourceful, can use the clinical decision tools and achieve maximum benefits.
FEMaLe brings forward a deeper understanding of complex diseases, not just endometriosis. We will co-create tools with healthcare providers to help stratify patients for more accurate diagnosis and to personalize the selection of the best drugs.
Based on estimations from health maintenance organizations, FEMaLe expects to be able to reduce overall cost of endometriosis treatment by at least 20%, facilitated by shared decision making, while improving patient outcomes.