{"id":36043,"date":"2023-08-14T09:35:28","date_gmt":"2023-08-14T08:35:28","guid":{"rendered":"https:\/\/www.innovationnewsnetwork.com\/?p=36043"},"modified":"2023-08-14T09:35:28","modified_gmt":"2023-08-14T08:35:28","slug":"the-female-project-finding-endometriosis-using-machine-learning","status":"publish","type":"post","link":"https:\/\/www.innovationnewsnetwork.com\/the-female-project-finding-endometriosis-using-machine-learning\/36043\/","title":{"rendered":"The FEMaLe project: Finding Endometriosis using Machine Learning"},"content":{"rendered":"

The FEMaLe project is working on realising a Machine Learning multi-omics platform that can analyse omics datasets and feed the information into a personalised predictive model.<\/h2>\n

European healthcare systems are under pressure from megatrends, including an increasingly ageing population. This means that a growing number of people are confronted with having often multiple chronic conditions.<\/p>\n

In addition, they are faced with the lack of coherent monitoring, collection, and data usage to support clinical decisions and treatments, reducing patient adverse events, diagnostic delay, misdiagnosis, or lack of a diagnosis altogether.<\/p>\n

As a result, systems are currently responsive rather than preventive.<\/p>\n

Without any action towards personalised early risk prediction, prevention, and co-ordinated intervention, systems will face many more patients suffering from chronic diseases. This includes multimorbidity, reducing quality of life and causing healthcare costs to rise.<\/p>\n

The ongoing \u2018Finding Endometriosis using Machine Learning\u2019 (FEMaLe) project builds upon the report from the European Commission (EC) on State of Health in the European Union (EU), which argues that prevention and primary care are the two major priorities in this decade.<\/p>\n

Preventive responsive actions to people suffering from diseases, including endometriosis, will greatly optimise quality of life and reduce healthcare costs, e.g., through fewer surgeries, hospitalisations, and rehabilitation programmes.<\/p>\n

Detailed clinical and psychosocial phenotyping of people with endometriosis, transformed into clinical decision support tools, could be a first step towards achieving earlier detection, avoiding unnecessary laparoscopies. Just imagine if endometriosis could be correctly diagnosed sooner and treated to bring this burden down \u2013 not only for those affected by endometriosis, but also for society in general.<\/p>\n

The FEMaLe project will develop and validate a Machine Learning (ML) multi-omic platform that can convert population datasets, phenotypic and genotypic, into a comprehensive and personalised predictive model to improve intervention along the continuum of care for people with endometriosis.<\/p>\n

Endometriosis: The last health taboo<\/h3>\n

The World Health Organization (WHO) recognises the importance of endometriosis and its impact on people\u2019s sexual and reproductive health, quality of life, and overall wellbeing. Endometriosis affects approximately one in ten (190 million) women and people assigned female at birth worldwide.<\/p>\n

It is a chronic condition marked by the presence of endometrial-like tissue outside the uterus, which in many patients is associated with debilitating and painful symptoms. It takes an average of 7.5 years from the onset of symptoms to diagnosis, which has a negative impact on health-related quality of life.<\/p>\n

Patients with endometriosis are also at greater risk of infertility, emergence of fatigue, multisite pain, and other comorbidities. Thus, endometriosis can negatively affect every aspect of a patient\u2019s daily life, including sexual relations, appetite, exercise, sleep, emotional wellbeing, social activities, childcare, and work and household productivity.<\/p>\n

The \u2018EndoCost Quality of Life Study\u2019 demonstrated that the average annual healthcare costs are three times higher for people with endometriosis compared with people without, even five years before and five years after diagnosis. The total workplace productivity loss averages 11 hours per week, with most of that loss caused by presenteeism.<\/p>\n

Endometriosis affects people during the prime years of their lives \u2013 a time when they should be finishing education, starting and maintaining a career, building relationships and having a family.<\/p>\n

Despite high prevalence and costs, endometriosis remains underfunded and under-researched, greatly limiting our basic understanding of the disease, and slowing much-needed innovation in diagnostic and treatment options.<\/p>\n

The FEMaLe project<\/h3>\n

The FEMaLe H2020 project<\/a> is a European project with international impact, driven by megatrends (increasingly ageing population, unharnessed health data value creation, and diagnostic delay), seeking to deliver a comprehensive model for personalised early risk prediction, prevention, and intervention for people with endometriosis, based on state-of-the-art Big Data technologies.<\/p>\n

Complex diseases, such as endometriosis, are driven by multiple networks of interconnected causative factors and metabolic processes. Patients\u2019 disease risks, rate of progression, and responses to therapy vary due to combinations of their mutations, predisposing phenotypic features, and environmental influences.<\/p>\n

A brand-new generation of Machine Learning and multifactorial data analytics methods are enabling the FEMaLe project to start untangling the disease risk, progression, and therapy response signatures inherent in large population datasets.<\/p>\n

Ultimately, the FEMaLe project will develop and demonstrate more clinical decision support tools, based on high-resolution stratification of people with endometriosis and Deep Learning in medical imaging to enable non-invasive diagnostics.<\/p>\n

\"The
Fig.1: The FEMale Framework<\/figcaption><\/figure>\n

Stratifying patient populations<\/h3>\n

Improvements in analytical methods enable the disentangling of complex diseases into distinct patient subgroups that have different root causes and influences, as proposed by PrecisionLife<\/a>, a UK-based pioneering techbio company.<\/p>\n

The key to understanding diseases at a deeper level is to find combinations of these factors (disease signatures) that distinguish one patient subgroup from another. This provides a more granular way of segregating patients, giving a higher-resolution view of the disease and opportunities to treat subgroups of patients with better, more personalised precision medicines.<\/p>\n

Analysing Danish and UK biobank data with the PrecisionLife combinatorial analytics platform recently achieved two firsts in endometriosis:<\/p>\n