Periodic Reporting for period 3 - FEMaLe (Finding Endometriosis using Machine Learning)
Période du rapport: 2024-01-01 au 2024-12-31
The framework 'P4 Medicine' (predictive, preventative, personalized, participatory) was developed to detect and prevent disease through close monitoring, deep statistical analysis, biomarker testing, and patient health coaching to best use the limited healthcare resources and produce maximum benefit for all patients. However, we have seen only few feasible examples over the past 10 years.
The Finding Endometriosis using Machine Learning (FEMaLe) project will revitalise the concept to develop and demonstrate the Scalable Multi-Omics Platform (SMOP) 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.
We will rely on participatory processes, advanced computer sciences, state-of-the-art technologies, and patient-shared data to deliver:
• Mobile health app for people with endometriosis.
• Clinical decision support (CDS) tools for targeted healthcare providers (risk stratification tool for general practitioners, multi-marker signature tool for gynaecologists, and non-invasive diagnostic tool for radiologist).
• Computer vision-based software tool for real time augmented reality guided surgery of endometriosis.
Health maintenance organisations (HMO) expect to be able to reduce overall cost of treatment by at least 20%, while improving patient outcomes, using CDS tools. The SMOP will be based on open protocol, embedded in all ethical and legal frameworks, to enable tailored and personalised usage to improve the lives of patients across Europe beyond the project period.
The Finding Endometriosis using Machine Learning (FEMaLe) project will revitalise the concept to develop and demonstrate the Scalable Multi-Omics Platform (SMOP) 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.
We will rely on participatory processes, advanced computer sciences, state-of-the-art technologies, and patient-shared data to deliver:
• Mobile health app for people with endometriosis.
• Clinical decision support (CDS) tools for targeted healthcare providers (risk stratification tool for general practitioners, multi-marker signature tool for gynaecologists, and non-invasive diagnostic tool for radiologist).
• Computer vision-based software tool for real time augmented reality guided surgery of endometriosis.
Health maintenance organisations (HMO) expect to be able to reduce overall cost of treatment by at least 20%, while improving patient outcomes, using CDS tools. The SMOP will be based on open protocol, embedded in all ethical and legal frameworks, to enable tailored and personalised usage to improve the lives of patients across Europe beyond the project period.
The FEMaLe project has benefited greatly from applying the Half Double Methodology to improve collaboration, communication, and cross-disciplinary understanding (WP1 and WP10). Systematic data collection enabled real-time progress tracking and adaptation, fostering early value creation and better project outcomes. A 'Reflective Learning Evaluation Framework' combined realistic evaluation with a learning perspective, reducing complexity, shortening time to impact, and strengthening trust and stakeholder engagement. This approach established a model for balancing accountability with adaptability, improving governance in complex research projects.
The project successfully developed ethical, gender-inclusive, and open science frameworks (WP2), supporting responsible research and innovation for endometriosis diagnosis and care. Multiple white papers were published, and stakeholder engagement ensured alignment with EU principles of inclusivity and innovation.
Large-scale epidemiological research (WP3) in Denmark and the UK advanced understanding of endometriosis prevalence, symptoms, and diagnostic delays, highlighting regional disparities and socioeconomic consequences. Findings were widely disseminated, including at major conferences, with award-winning research presented in 2024. Machine learning models for early detection using health records further demonstrated potential for clinical application.
The project also made significant contributions to genetic research (WP4), developing a novel risk classifier and identifying genetic subtypes linked to proteomic markers, such as PAEP and LPA. A clinical decision support tool prototype integrating genetic, proteomic, and clinical data marked a step toward precision medicine. These achievements were shared at high-profile international conferences, enhancing awareness and impact.
Digital health innovation was advanced through Lucy App (WP5), which collected over 3 million data entries from more than 20,000 users across Europe, creating one of the largest prospective data banks for endometriosis research. Machine learning confirmed the reliability of self-reported data, and a longitudinal study identified symptom patterns, quality-of-life impacts, and environmental factors. A digital psychological intervention, MY-ENDO (WP8), was developed and integrated into Lucy App, demonstrating effectiveness in improving emotional resilience and self-management, supported by a successful randomised controlled trial.
AI and augmented reality (AR) technologies significantly improved laparoscopic diagnosis and treatment of endometriosis. Advanced lesion detection algorithms (WP6) enhanced real-time surgical precision, particularly benefiting junior surgeons, and were validated through structured interviews and video-based assessments. The integration of these algorithms into AR-assisted surgical workflows set a new standard for AI-driven surgery (WP7), with strong clinician feedback confirming safety and usability. Dissemination included conference presentations, webinars, and planned journal publications.
The project’s outreach and dissemination activities achieved an organic reach of over 20 million views by 2024 (WP9), driven by popularised publications, media appearances, social media campaigns, conference participation, scientific publications, and engagement through the Lucy app. This exceptional reach significantly boosted awareness and engagement with FEMaLe’s results across diverse audiences.
The project successfully developed ethical, gender-inclusive, and open science frameworks (WP2), supporting responsible research and innovation for endometriosis diagnosis and care. Multiple white papers were published, and stakeholder engagement ensured alignment with EU principles of inclusivity and innovation.
Large-scale epidemiological research (WP3) in Denmark and the UK advanced understanding of endometriosis prevalence, symptoms, and diagnostic delays, highlighting regional disparities and socioeconomic consequences. Findings were widely disseminated, including at major conferences, with award-winning research presented in 2024. Machine learning models for early detection using health records further demonstrated potential for clinical application.
The project also made significant contributions to genetic research (WP4), developing a novel risk classifier and identifying genetic subtypes linked to proteomic markers, such as PAEP and LPA. A clinical decision support tool prototype integrating genetic, proteomic, and clinical data marked a step toward precision medicine. These achievements were shared at high-profile international conferences, enhancing awareness and impact.
Digital health innovation was advanced through Lucy App (WP5), which collected over 3 million data entries from more than 20,000 users across Europe, creating one of the largest prospective data banks for endometriosis research. Machine learning confirmed the reliability of self-reported data, and a longitudinal study identified symptom patterns, quality-of-life impacts, and environmental factors. A digital psychological intervention, MY-ENDO (WP8), was developed and integrated into Lucy App, demonstrating effectiveness in improving emotional resilience and self-management, supported by a successful randomised controlled trial.
AI and augmented reality (AR) technologies significantly improved laparoscopic diagnosis and treatment of endometriosis. Advanced lesion detection algorithms (WP6) enhanced real-time surgical precision, particularly benefiting junior surgeons, and were validated through structured interviews and video-based assessments. The integration of these algorithms into AR-assisted surgical workflows set a new standard for AI-driven surgery (WP7), with strong clinician feedback confirming safety and usability. Dissemination included conference presentations, webinars, and planned journal publications.
The project’s outreach and dissemination activities achieved an organic reach of over 20 million views by 2024 (WP9), driven by popularised publications, media appearances, social media campaigns, conference participation, scientific publications, and engagement through the Lucy app. This exceptional reach significantly boosted awareness and engagement with FEMaLe’s results across diverse audiences.
The FEMaLe project has extended its impact through high-profile engagement activities, including citizen science seminars, roundtables, and an international conference, which translated project findings into actionable strategies for improving early endometriosis diagnosis.
By embedding Responsible Research and Innovation principles, FEMaLe fostered multi-stakeholder collaboration, public-private partnerships, and awareness campaigns, ensuring ethical and socially responsible frameworks with lasting impact.
Project findings directly informed expert counsels at the Danish Parliament and received wide media coverage, bridging scientific discovery and public understanding. These efforts laid a foundation for more targeted diagnostic tools and policy-relevant solutions for endometriosis care.
Public dissemination efforts, such as podcasts and exhibitions like The Overlooked Body, increased awareness of endometriosis, while combining epidemiological research, data analysis, and outreach significantly advanced knowledge, policy, and healthcare practices.
The Lucy App demonstrated the potential of mobile health technologies to improve symptom tracking, early detection, and personalised management. Plans are underway to evolve the app into a sustainable research platform, enhancing its role in digital health innovation.
AI-assisted surgical technologies and digital health innovations have set new benchmarks in endometriosis care, reducing diagnostic errors, improving surgical precision, and enabling scalable future applications.
FEMaLe also highlighted the importance of integrating psychological support into standard care, with plans to scale digital mental health interventions to ensure holistic management.
Overall, FEMaLe has shaped the future of endometriosis research and digital health, creating a strong foundation for two major follow-up EU-funded projects: EUmetriosis (€7M) and READI (€66.8M) advancing women’s health and inclusive research across Europe.
By embedding Responsible Research and Innovation principles, FEMaLe fostered multi-stakeholder collaboration, public-private partnerships, and awareness campaigns, ensuring ethical and socially responsible frameworks with lasting impact.
Project findings directly informed expert counsels at the Danish Parliament and received wide media coverage, bridging scientific discovery and public understanding. These efforts laid a foundation for more targeted diagnostic tools and policy-relevant solutions for endometriosis care.
Public dissemination efforts, such as podcasts and exhibitions like The Overlooked Body, increased awareness of endometriosis, while combining epidemiological research, data analysis, and outreach significantly advanced knowledge, policy, and healthcare practices.
The Lucy App demonstrated the potential of mobile health technologies to improve symptom tracking, early detection, and personalised management. Plans are underway to evolve the app into a sustainable research platform, enhancing its role in digital health innovation.
AI-assisted surgical technologies and digital health innovations have set new benchmarks in endometriosis care, reducing diagnostic errors, improving surgical precision, and enabling scalable future applications.
FEMaLe also highlighted the importance of integrating psychological support into standard care, with plans to scale digital mental health interventions to ensure holistic management.
Overall, FEMaLe has shaped the future of endometriosis research and digital health, creating a strong foundation for two major follow-up EU-funded projects: EUmetriosis (€7M) and READI (€66.8M) advancing women’s health and inclusive research across Europe.