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FEMaLe: The use of ...
FEMaLe: The use of machine learning for early diagnosis of endometriosis based on patient self-reported data—Study protocol of a multicenter trial
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- Balogh, Dora B. (författare)
- Department of Obstetrics and Gynecology, Semmelweis University, Budapest, Hungary
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- Hudelist, Gernot (författare)
- Department of Gynecology, Center for Endometriosis, Hospital St. John of God, Vienna, Austria; Rudolfinerhaus Private Clinic and Campus, Vienna, Austria
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- Bļizņuks, Dmitrijs (författare)
- Department of Computer Control and Computer Networks, Riga Technical University, Riga, Latvia
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- Raghothama, Jayanth (författare)
- KTH,Hälsoinformatik och logistik
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- Becker, Christian M. (författare)
- Oxford Endometriosis CaRe Centre, Nuffield Department of Women’s and Reproductive Health, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
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- Horace, Roman (författare)
- Franco-European Multidisciplinary Endometriosis Institute (IFEMEndo), Clinique Tivoli-Ducos, Bordeaux, France, Clinique Tivoli-Ducos
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- Krentel, Harald (författare)
- Department of Obstetrics, Gynecology, Gynecologic Oncology and Senology, Bethesda Hospital Duisburg, Duisburg, Germany
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- Horne, Andrew W. (författare)
- Centre for Reproductive Health, University of Edinburgh, Institute of Inflammation and Repair, Edinburgh, United Kingdom
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- Bourdel, Nicolas (författare)
- Department of Surgical Gynecology, University of Clermont Auvergne, Clermont-Ferrand, France
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- Marki, Gabriella (författare)
- MedEnd Institute, Budapest, Hungary
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- Tomassetti, Carla (författare)
- Leuven University Endometriosis Center of Expertise, Leuven University Fertility Center, Department of Obstetrics and Gynecology, UZ Gasthuisberg, Leuven, Belgium
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- Kirk, Ulrik Bak (författare)
- Department of Public Health, Aarhus University, Aarhus, Denmark; Research Unit for General Practice, Aarhus, Denmark
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- Acs, Nandor (författare)
- Department of Obstetrics and Gynecology, Semmelweis University, Budapest, Hungary
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- Bokor, Attila (författare)
- Department of Obstetrics and Gynecology, Semmelweis University, Budapest, Hungary
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Department of Obstetrics and Gynecology, Semmelweis University, Budapest, Hungary Department of Gynecology, Center for Endometriosis, Hospital St John of God, Vienna, Austria; Rudolfinerhaus Private Clinic and Campus, Vienna, Austria (creator_code:org_t)
- Public Library of Science (PLoS), 2024
- 2024
- Engelska.
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 19:5 May
- Relaterad länk:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
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- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Reproduktionsmedicin och gynekologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Obstetrics, Gynaecology and Reproductive Medicine (hsv//eng)
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- Av författaren/redakt...
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Balogh, Dora B.
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Hudelist, Gernot
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Bļizņuks, Dmitri ...
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Raghothama, Jaya ...
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Becker, Christia ...
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Horace, Roman
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Krentel, Harald
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Horne, Andrew W.
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Bourdel, Nicolas
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Marki, Gabriella
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Tomassetti, Carl ...
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Kirk, Ulrik Bak
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Acs, Nandor
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Bokor, Attila
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