EndometriosisMLEndoNCT06147687
Machine Learning for Early Diagnosis of Endometriosis(MLEndo)
An observational study of Endometriosis and Pelvic Pain, run by Semmelweis University at 2 sites in Hungary.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 14 to 45 years, women onlyHealthy volunteers can take part
- Conditions the study is for
- Endometriosis, Pelvic Pain, Infertility, Female
- What is being tested?
- Self reported data collection
- How many people
- 10,000 participants
- Where
- 2 sitesHungary
- Study dates
- 1 Jan 2022to 31 Dec 2024
- How long will I take part?
- 24 Months
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 14 to 45 years
- Women only
- Healthy volunteers accepted
- Child, Adult
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.
Who may be able to join
- Women in reproductive age
- 5000 patients with endometriosis
- 5000 patients without endometriosis
Who cannot join
- Ongoing pregnancy
- Malignant condition of ovary/uterus/breast
Trial locations and contacts
2 sites in 1 country.
Hungary2 sites, 2 recruiting
- Recruiting
Bokor Attila
Budapest, 1028
Bokor Attila, 06703118868, attila.z.bokor@gmail.com
- Recruiting
Semmelweis University
Budapest, 1088
Attila Bokor, MD, PhD, +36703118868, attila.z.bokor@gmail.com
Dora Balogh, PhD, +3604591500 ext. Bokor, attila.z.bokor@gmail.com
About this study
Official title: 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.
Read the 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
- Conditions
- Endometriosis, Pelvic Pain, Infertility, Female
- Being tested
- Self reported data collection
- Keywords
- endometriosis, infertility
- Medical subject headings
- Endometriosis, Pelvic Pain, Infertility, Female, Infertility
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Self reported data collectionDiagnostic test
Also called ML assessement
ML assessement of colleceted data
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: Self reported data collection | |
| 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. Receives: Diagnostic Test: Self reported data collection |
What is being measured
The primary outcome is the main question the study is designed to answer.
Patient- profiling using the Lucy app
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)
Measured: 24 month
Secondary outcomes (1)
Impact of diet and lifestyle on the development of endometriosis
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
Measured: 24 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Nov 2023
- Study start1 Jan 2022
- Primary completion31 Dec 2024 (estimated)
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for EndometriosisStudies of the same condition, open and under way.
- More trials run by Semmelweis UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 10,000 participants (estimated)
- Target duration
- 24 Months
Sponsor and investigators
- Lead sponsor
- Semmelweis University
- Sponsor type
- University, hospital or other
- Collaborators
- University of Aarhus
- Responsible party
- Sponsor
- Investigators
- Attila Bokor, Principal investigator, Semmelweis University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06147687
- Sponsor's protocol ID
- Semmelweis
- Acronym
- MLEndo
- Status last verified
- Sep 2023
- First submitted
- 25 Sep 2023
- Last updated
- 28 Nov 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: In case of any interest we are happy to share IPD
Study at a glance
From the registry record, last updated by the sponsor on 28 Nov 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- FEMaLe: The Use of Machine Learning for Early Diagnosis of Endometriosis Based on Patient Self-reported Data - Study Protocol of a Multicenter Trial
- ClinicalTrials.gov ID
- NCT06147687
- Acronym / sponsor ID
- MLEndo / Semmelweis
- Conditions
- Endometriosis, Pelvic Pain, Infertility, Female
- Keywords
- endometriosis, infertility
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- Semmelweis UniversityUniversity, hospital or other
- Collaborators
- University of Aarhus
- Study start
- 1 Jan 2022 (actual)
- Primary completion
- 31 Dec 2024 (estimated)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 28 Nov 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 24 Months
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Patients with endometriosis and Healthy controls | – | Diagnostic Test: Self reported data collection5 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. |
| Control | – | Diagnostic Test: Self reported data collection5 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. |
Who may qualify
- Ages
- 14 to 45 years
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
- 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.
Key inclusion criteria (3 listed)
- Women in reproductive age
- 5000 patients with endometriosis
- 5000 patients without endometriosis
Key exclusion criteria (2 listed)
- Ongoing pregnancy
- Malignant condition of ovary/uterus/breast
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Self reported data collectionAlso: ML assessementML assessement of colleceted data | Diagnostic test | Control, Patients with endometriosis and Healthy controls |
Primary outcome
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)
1 secondary outcome
Impact of diet and lifestyle on the development of endometriosis
Time frame: 24 month
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Nov 2023.
- Individual participant dataSharing planned: Yes. In case of any interest we are happy to share IPD
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Attila Bokor principal investigator
Semmelweis University
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Endometriosis and Pelvic Pain, run by Semmelweis University at 2 sites in Hungary.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 28 Nov 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Hungary. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Attila Bokor (703118868 ext. 0036, attila.z.bokor@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Patient- profiling using the Lucy app (time frame: 24 month). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Nov 2023.