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Finding Endometriosis using Machine Learning

Project: Research

  • Kirk, Ulrik Bak (PI)
  • Nielsen, Ole Bækgaard (Award holder)
  • Nyegaard, Mette (CoPI)
  • Rytter, Dorte (CoPI)
  • Hansen, Karina Ejgaard (Participant)
  • Forman, Axel (Participant)
  • Horne, Andrew W (Participant)
  • Saunders, Philippa T (Participant)
  • Saraswat, Lucky (Participant)
  • Bokor, Attila (Participant)
  • Barko, Boglarka (Participant)
  • Zondervan, Krina T. (Participant)
  • Becker, Christian M. (Participant)
  • Rahmioglu, Nilufer (Participant)
  • Bourdel, Nicolas (Participant)
  • Rémy, Benjamin (Participant)
  • Bliznuks, Dmitrijs (Participant)
  • Meijer, Sebastiaan (Participant)
  • Raghothama, Jayanth (Participant)
  • Sales da Silva, Bruno (Participant)
  • Møller, Gert Lykke (Participant)
  • Sziráczki, Norbert (Participant)
  • Goethe, Ole (Participant)
  • Djokic, Katarina (Participant)
  • Todic, Nemanja (Participant)
  • Salamon, Adrienn (Participant)
  • Centre for Pelvic Pain and Endometriosis, University of Edinburgh
  • Semmelweis University
  • The University of Oxford
  • PRECISIONLIFE LTD
  • Aberdeen Endometriosis Centre
  • SurgAR
  • Riga Technical University
  • Kungliga Tekniska Högskolan
  • ISTANBUL AVRUPA ARASTIRMALARI DERNEGI
  • YOURCODE LAB
  • CORRELATE AS
  • NEMANJA TODIC PREDUZETNIK WEB BAY
  • EGYUTT KONNYEBB NOI EGESZSEGERT ALAPITVANY
See relations at Aarhus University

Description

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.

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.

Key findings

We will rely on participatory processes, advanced computer sciences, state-of-the-art technologies, and patient-shared data to deliver:

1) mobile health app for people with endometriosis,

2) three 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), and

3) 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.

Layman's description

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.
Short titleFEMaLe
AcronymFEMaLe
StatusActive
Effective start/end date01/01/202131/12/2024

Activities

  • FEMaLe Kick-Off Meeting

    Activity: Participating in or organising an event typesParticipation in or organisation of workshop, seminar or course

Press/Media

ID: 217020303