Machine Learning for Early Diagnosis of Endometriosis(MLEndo) (MLEndo)

November 21, 2023 updated by: Semmelweis University

FEMaLe: The Use of Machine Learning for Early Diagnosis of Endometriosis Based on Patient Self-reported Data - Study Protocol of a Multicenter Trial

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Introduction: Endometriosis is a complex and chronic disease that affects ∼176 million women of reproductive age and remains largely unresolved. It is defined by the presence of endometrium-like tissue outside the uterus and is commonly associated with chronic pelvic pain, infertility, and decreased quality of life. Despite numerous proposed screening and triage methods such as biomarkers, genomic analysis, imaging techniques, and questionnaires to replace invasive diagnostic laparoscopy, none have been widely adopted in clinical practice.

. 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

Study Type

Observational

Enrollment (Estimated)

10000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Hungary
      • Budapest, Hungary, 1088
        • Recruiting
        • Semmelweis University
        • Contact:
        • Contact:
        • Principal Investigator:
          • Attila Bokor, MD PhD
      • Budapest, Hungary, 1028

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

5000/5000 patienets with/without endometriosis This study is conducted as a multicenter, parallel-group trial. Study participants are being recruited through the Lucy app. Data are collected in the following countries: Hungary, Denmark, Sweden, Germany, and Austria. Participant recruitment began in December 2021 and is expected to continue until December 2024.

Description

Inclusion Criteria:

  • Women in reproductive age
  • 5000 patients with endometriosis
  • 5000 patients without endometriosis

Exclusion Criteria:

  • Ongoing pregnancy
  • Malignant condition of ovary/uterus/breast

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
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.
Diagnostic test: Self reported data collection
ML assessement of colleceted data
Other Names:
  • ML assessement
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.
Diagnostic test: Self reported data collection
ML assessement of colleceted data
Other Names:
  • ML assessement

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Patient- profiling using the Lucy app
Time Frame: 24 month

Establish a comprehensive and extensive prospective big data repository using the Lucy app. This initiative aims to identify unique clinical cohorts by leveraging various factors such as digital footprints, symptoms, patient experiences, comorbidities, clinical severity, and lifestyle patterns. By employing Using ML for big data analysis, authors can build patient profiles and structured clinical data that facilitate the early detection of endometriosis with pelvic pain.

Self-reported data of the participants will be measured as follows:

  • Evaluating the quality of life using the 5-level EQ-5D (EQ-5D-5L)
  • Endometriosis Health Profile 5 (EHP-5) .
  • Pain scores using the Visual Analogue Scale (VAS) .
  • Central pain sensitization using the short version of Central Sensitization Inventory (CSI-9)
24 month

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Impact of diet and lifestyle on the development of endometriosis
Time Frame: 24 month

Additionally, authors can identify nutritional components that may worsen the quality of life and pain in women with endometriosis; thus, they can create evidence-based dietary recommendations.

The changes in quality of life will be assessed by using Self-reported data of the participants will be measured as follows:

Change From Baseline in Pain Scores on the Visual Analog Scale at 12 months. Changes from baseline values on EHP5 at 12 months
24 month

Other Outcome Measures

Outcome Measure
Measure Description
Time Frame
Economical burden of endometriosis
Time Frame: 24 month
Economical burden taking into account the cost of diet and healthcare use. The exact cost of endometriosis related diet will be reported per month in EUR.
24 month

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Semmelweis University

Collaborators

University of Aarhus

Investigators

  • Principal Investigator: Attila Bokor, Semmelweis University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2022

Primary Completion (Estimated)

December 31, 2024

Study Completion (Estimated)

December 31, 2024

Study Registration Dates

First Submitted

September 25, 2023

First Submitted That Met QC Criteria

November 21, 2023

First Posted (Actual)

November 28, 2023

Study Record Updates

Last Update Posted (Actual)

November 28, 2023

Last Update Submitted That Met QC Criteria

November 21, 2023

Last Verified

September 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • Semmelweis

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

In case of any interest we are happy to share IPD

IPD Sharing Time Frame

Data will become available from 01.01.2025 for 5 years

IPD Sharing Access Criteria

Data will be available for researchers in the field

IPD Sharing Supporting Information Type

  • STUDY_PROTOCOL

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on Endometriosis

Clinical Trials on Self reported data collection

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in Gabon

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

Subscribe