Clinical trial NCT06147687: Machine Learning for Early Diagnosis of Endometriosis(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
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Endometriosis
Pelvic Pain
Infertility, Female
Keywords: endometriosis
infertility
Lead sponsor: Semmelweis University
Sponsor class: OTHER
Collaborators: name: University of Aarhus
First posted: 2023-11-28
Enrollment: 10000
Sex: FEMALE
Minimum age (years): 14
Maximum age (years): 45
Standard ages: CHILD
ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

Brief 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.

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

Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

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

Primary outcomes:
measure: Patient- profiling using the Lucy app
timeFrame: 24 month
Secondary outcomes:
measure: Impact of diet and lifestyle on the development of endometriosis
timeFrame: 24 month
Other outcomes:
measure: Economical burden of endometriosis
timeFrame: 24 month
Locations:
city: Budapest
country: Hungary
facility: Bokor Attila
city: Budapest
country: Hungary
facility: Semmelweis University
Study officials:
affiliation: Semmelweis University
name: Attila Bokor
role: PRINCIPAL_INVESTIGATOR
References: