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

Journal: PloS one
PMID: 38722932

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.

Authors

  • Dora B Balogh
    Department of Obstetrics and Gynecology, Semmelweis University, Budapest, Hungary.
  • Gernot Hudelist
    Department of Gynecology, Center for Endometriosis, Hospital St. John of God, Vienna, Austria.
  • Dmitrijs Bļizņuks
    Institute of Smart Computer Technologies, Riga Technical University, LV-1658 Riga, Latvia.
  • Jayanth Raghothama
    Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden.
  • Christian M Becker
    Endometriosis CaRe Centre Oxford, Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom.
  • Roman Horace
    Franco-European Multidisciplinary Endometriosis Institute (IFEMEndo), Clinique Tivoli-Ducos, Bordeaux, France.
  • Harald Krentel
    Department of Obstetrics, Gynecology, Gynecologic Oncology and Senology, Bethesda Hospital Duisburg, Duisburg, Germany.
  • Andrew W Horne
    Centre for Reproductive Health, University of Edinburgh, Institute of Inflammation and Repair, Edinburgh, United Kingdom.
  • Nicolas Bourdel
    Department of Gynaecological Surgery, CHU Clermont-Ferrand, 1 Place Lucie et Raymond Aubrac, 63000, Clermont-Ferrand, France. nicolas.bourdel@gmail.com.
  • Gabriella Marki
    MedEnd Institute, Budapest, Hungary.
  • Carla Tomassetti
    Leuven University Endometriosis Center of Expertise, Leuven University Fertility Center, Department of Obstetrics and Gynecology, UZ Gasthuisberg, Leuven, Belgium.
  • Ulrik Bak Kirk
    Department of Public Health, Aarhus University, Aarhus, Denmark.
  • Nándor Ács
    Centre for Translational Medicine, Semmelweis University, Üllői út 26, 1082, Budapest, Hungary.
  • Attila Bokor
    Department of Obstetrics and Gynecology, Semmelweis University, Budapest, Hungary.

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