Machine Learning for Early Diagnosis of Endometriosis(MLEndo)

Endometriosis Clinical Trial

— MLEndo  

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06147687
• Other study ID #: Semmelweis
• Status: Recruiting
• Start date: January 1, 2022
• Est. completion date: December 31, 2024

Study information

• Verified date: September 2023
• Source: Semmelweis University
• Contact: Attila Bokor
• Phone: 703118868
• Email: attila.z.bokor[@]gmail.com
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical 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. In addition, authors will investigate the association of removed or restricted dietary components with quality of life, pain, and central sensitization.

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

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 10000
• Est. completion date: December 31, 2024
• Est. primary completion date: December 31, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: 14 Years to 45 Years

Eligibility

Inclusion Criteria:

• Women in reproductive age

• 5000 patients with endometriosis

• 5000 patients without endometriosis

Exclusion Criteria:

• Ongoing pregnancy

• Malignant condition of ovary/uterus/breast

Related Conditions & MeSH terms

• Endometriosis
• Infertility
• Infertility, Female
• Pelvic Pain

Intervention

Diagnostic Test:
• Self reported data collection
ML assessement of colleceted data

Locations

Country	Name	City	State
Hungary	Bokor Attila	Budapest
Hungary	Semmelweis University	Budapest

Sponsors (2)

Lead Sponsor	Collaborator
Semmelweis University	University of Aarhus

Country where clinical trial is conducted

Hungary, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	Economical burden of endometriosis	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
Primary	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)	24 month
Secondary	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	24 month