Ignite Creation Date:
2024-05-06 @ 7:48 PM
Last Modification Date:
2024-10-26 @ 3:14 PM
Study NCT ID:
NCT06147687
Status:
RECRUITING
Last Update Posted:
2023-11-28
First Post:
2023-09-25
Brief Title:
Machine Learning for Early Diagnosis of EndometriosisMLEndo
Sponsor:
Semmelweis University
Organization:
Semmelweis University
Study Overview
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
Status:
RECRUITING
Status Verified Date:
2023-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
MLEndo
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 eg 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 5000 women with endometriosis and 5000 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
Despite the availability of various screening methods eg 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 5000 women with endometriosis and 5000 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None