Machine Learning for Early Diagnosis of Endometriosis(MLEndo)
NCT ID: NCT06147687
Last Updated: 2023-11-28
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
10000 participants
OBSERVATIONAL
2022-01-01
2024-12-31
Brief Summary
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Detailed Description
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. Despite the availability of various screening methods (e.g., biomarkers, genomic analysis, imaging techniques) that are intended to replace the need for invasive diagnostic laparoscopy, the time to diagnosis remains in the range of 4 to 11 years. Aims: The project aims to create a large prospective data bank using the Lucy medical mobile application and collect and analyze patient profiles and structured clinical data with artificial intelligence. In addition, authors will investigate the association of removed or restricted dietary components with quality of life, pain, and central sensitization. Methods: A Baseline and Longitudinal Questionnaire in the Lucy app collects self-reported information on symptoms related to endometriosis, socio-demographics, mental and physical health, nutritional, and other lifestyle factors. 5,000 women with endometriosis and 5,000 women in a control group will be enrolled and followed up for one year. With this information, any connections between symptoms and endometriosis will be analyzed with machine learning. Conclusions: Authors can 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, authors can identify nutritional components that may worsen the quality of life and pain in women with endometriosis; thus, authors can create evidence-based dietary recommendations.
Keywords: Endometriosis, Machine learning, Non-invasive diagnosis, Diet
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Patients with endometriosis and Healthy controls
5 000 people with endometriosis will be enrolled and followed up for 1one year. To participate in the study, the women must meet the inclusion criteria.
Self reported data collection
ML assessement of colleceted data
Control
5 000 people in a control group will be enrolled and followed up for 1one year. To participate in the study, the women must meet the inclusion criteria.
Self reported data collection
ML assessement of colleceted data
Interventions
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Self reported data collection
ML assessement of colleceted data
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
* 5000 patients with endometriosis
* 5000 patients without endometriosis
Exclusion Criteria
* Malignant condition of ovary/uterus/breast
14 Years
45 Years
FEMALE
Yes
Sponsors
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University of Aarhus
OTHER
Semmelweis University
OTHER
Responsible Party
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Principal Investigators
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Attila Bokor
Role: PRINCIPAL_INVESTIGATOR
Semmelweis University
Locations
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Bokor Attila
Budapest, , Hungary
Semmelweis University
Budapest, , Hungary
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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Semmelweis
Identifier Type: -
Identifier Source: org_study_id