Trials Sponsors Conditions Drugs Pricing

Conditions

Endometriosis

Outcome Measures

Primary Outcomes (3)

Development and validation of an AI model for real-time automated detection of endometriosis lesions
* Based on laparoscopic image and video data. * Evaluation of model accuracy using the F1-score, with a target value of ≥ 0.7.
During time-span of study (approx. 1 year)
Quality of video anonymization
* Assessment of the effectiveness of the "InOut" AI model v0.2 in identifying and removing non-relevant image data. * Quality assurance through manual review of anonymized data. The videos are correlated with the following anonymized metadata, which are also transferred to KS * Age group of the patient (18-25; 25-30; 35-40,…) * Weight class of the patient (BMI \<17.5; 17.5-19; \>19-25; \>25-30; \>30) * Type of surgery (laparoscopy with or without treatment of endometriosis) * Total duration of the operation * Complications during the operation (yes/no) * Endoscopic devices used, especially the camera * Existing pathological findings related to endometriosis
During time-span of study (approx. 1 year)
Creation of a high-quality annotated image dataset for AI training
* Target: 80-90% of selected images should contain endometriosis lesions, with the remaining being negative samples. * Annotation performed by medical professionals Clinically trained personnel at the University Hospital Tübingen (UKT) select 300 varied JPEG images from each anonymized video for annotation. The aim is to include 80%-90% of images displaying endometriosis lesions, with the remainder depicting other tissue abnormalities or no lesions.
During time-span of study (approx. 1 year)

Study Arms (1)

Suspected endometriosis

Eligibility Criteria

Age18 Years+

Sexfemale

Healthy VolunteersNo

Age GroupsAdult (18-64), Older Adult (65+)

Sampling MethodProbability Sample

Study Population

Included are patients who present themselves at the University Women's Clinic as part of the outpatient clinic in the Endometriosis Center. Women with a suspected diagnosis or confirmed diagnosis of endometriosis who have an indication for laparoscopic assessment are included.

You may qualify if:

Age ≥ 18 years

Sponsors & Collaborators

University Hospital Tuebingen
Otherlead

Study Sites (1)

University Hospital Tuebingen, Department of Women's Health

Tübingen, 72076, Germany

MeSH Terms

Conditions

Endometriosis

Condition Hierarchy (Ancestors)

Genital Diseases, FemaleFemale Urogenital DiseasesFemale Urogenital Diseases and Pregnancy ComplicationsUrogenital DiseasesGenital Diseases

Enrollment

26 participants (actual enrollment reported by the registry).

Study Design

Study Type: observational
Observational Model: CASE ONLY
Time Perspective: PROSPECTIVE
Sponsor Type: OTHER
Responsible Party: SPONSOR

Study Record Dates

First Submitted

January 17, 2025

First Posted

March 12, 2025

Study Start

October 10, 2023

Actual (reported)

AI Based Real Time Detection of Endometriosis Lesions

Brief Summary

Registry Overview

Trial Relationships

Study Timeline

Study Start

First Submitted

Primary Completion

First Posted

Study Completion

Conditions

Outcome Measures

Primary Outcomes (3)

Study Arms (1)

Suspected endometriosis

Eligibility Criteria

You may qualify if:

Sponsors & Collaborators

Study Sites (1)

MeSH Terms

Conditions

Condition Hierarchy (Ancestors)

Enrollment

Study Design

Study Record Dates

You may not qualify if:

Source record

Locations

Brief Summary

Registry Overview

Trial Relationships

Related Scientific Literature

Study Timeline

Study Start

First Submitted

Primary Completion

First Posted

Study Completion

Conditions

Outcome Measures

Primary Outcomes (3)

Study Arms (1)

Suspected endometriosis

Eligibility Criteria

You may qualify if:

Sponsors & Collaborators

Study Sites (1)

MeSH Terms

Conditions

Condition Hierarchy (Ancestors)

Enrollment

Study Design

Study Record Dates

You may not qualify if:

Source record

Locations