FEMaLe: The use of machine learning for early diagnosis of endometriosis based on patient self-reported data-Study protocol of a multicenter trial

Dora B Balogh; Gernot Hudelist; Dmitrijs Bļizņuks; Jayanth Raghothama; Christian M Becker; Roman Horace; Harald Krentel; Andrew W Horne; Nicolas Bourdel; Gabriella Marki; Carla Tomassetti; Ulrik Bak Kirk; Nandor Acs; Attila Bokor

doi:10.1371/journal.pone.0300186

FEMaLe: The use of machine learning for early diagnosis of endometriosis based on patient self-reported data-Study protocol of a multicenter trial

PLoS One. 2024 May 9;19(5):e0300186. doi: 10.1371/journal.pone.0300186. eCollection 2024.

Authors

Dora B Balogh¹, Gernot Hudelist^{2

3}, Dmitrijs Bļizņuks⁴, Jayanth Raghothama⁵, Christian M Becker⁶, Roman Horace⁷, Harald Krentel⁸, Andrew W Horne⁹, Nicolas Bourdel¹⁰, Gabriella Marki¹¹, Carla Tomassetti¹², Ulrik Bak Kirk^{13

14}, Nandor Acs¹, Attila Bokor¹

Affiliations

¹ Department of Obstetrics and Gynecology, Semmelweis University, Budapest, Hungary.
² Department of Gynecology, Center for Endometriosis, Hospital St. John of God, Vienna, Austria.
³ Rudolfinerhaus Private Clinic and Campus, Vienna, Austria.
⁴ Department of Computer Control and Computer Networks, Riga Technical University, Riga, Latvia.
⁵ Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden.
⁶ Oxford Endometriosis CaRe Centre, Nuffield Department of Women's and Reproductive Health, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom.
⁷ Franco-European Multidisciplinary Endometriosis Institute (IFEMEndo), Clinique Tivoli-Ducos, Bordeaux, France.
⁸ Department of Obstetrics, Gynecology, Gynecologic Oncology and Senology, Bethesda Hospital Duisburg, Duisburg, Germany.
⁹ Centre for Reproductive Health, University of Edinburgh, Institute of Inflammation and Repair, Edinburgh, United Kingdom.
¹⁰ Department of Surgical Gynecology, University of Clermont Auvergne, Clermont-Ferrand, France.
¹¹ MedEnd Institute, Budapest, Hungary.
¹² Leuven University Endometriosis Center of Expertise, Leuven University Fertility Center, Department of Obstetrics and Gynecology, UZ Gasthuisberg, Leuven, Belgium.
¹³ Department of Public Health, Aarhus University, Aarhus, Denmark.
¹⁴ Research Unit for General Practice, Aarhus, Denmark.

Abstract

Introduction: Endometriosis is a chronic disease that affects up to 190 million women and those assigned female at birth and remains unresolved mainly in terms of etiology and optimal therapy. It is defined by the presence of endometrium-like tissue outside the uterine cavity and is commonly associated with chronic pelvic pain, infertility, and decreased quality of life. Despite the availability of various screening methods (e.g., biomarkers, genomic analysis, imaging techniques) intended to replace the need for invasive surgery, the time to diagnosis remains in the range of 4 to 11 years.

Aims: This study aims to create a large prospective data bank using the Lucy mobile health application (Lucy app) and analyze patient profiles and structured clinical data. In addition, we will investigate the association of removed or restricted dietary components with quality of life, pain, and central pain sensitization.

Methods: A baseline and a longitudinal questionnaire in the Lucy app collects real-world, self-reported information on symptoms of endometriosis, socio-demographics, mental and physical health, economic factors, nutritional, and other lifestyle factors. 5,000 women with confirmed endometriosis and 5,000 women without diagnosed endometriosis in a control group will be enrolled and followed up for one year. With this information, any connections between recorded symptoms and endometriosis will be analyzed using machine learning.

Conclusions: We aim to develop a phenotypic description of women with endometriosis by linking the collected data with existing registry-based information on endometriosis diagnosis, healthcare utilization, and big data approach. This may help to achieve earlier detection of endometriosis with pelvic pain and significantly reduce the current diagnostic delay. Additionally, we may identify dietary components that worsen the quality of life and pain in women with endometriosis, upon which we can create real-world data-based nutritional recommendations.

Copyright: © 2024 Balogh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Publication types

Research Support, Non-U.S. Gov't
Multicenter Study
Clinical Trial Protocol

MeSH terms

Adult
Early Diagnosis*
Endometriosis* / diagnosis
Female
Humans
Machine Learning*
Mobile Applications
Pelvic Pain / diagnosis
Prospective Studies
Quality of Life*
Self Report*

Grants and funding

This article is part of the project Finding Endometriosis using Machine Learning (FEMaLe), which has received funding from The European Union's Horizon 2020 research and innovation program (Grant no. 101017562). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.