AI Based Real Time Detection of Endometriosis Lesions

NCT06873373 | Completed

• 1 other identifier: MT_STORZ federated learning
• Study Type: observational
• Target: 26
• Locations: 1 country
• Sites: 1

Brief Summary

Development of AI-based approaches for automated real-time detection of endometriosis lesions using endoscopic image and video material.

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

• Age: 18 Years+
• Sex: female
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability 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
• Written consent after explanation
• Indication for surgical treatment of endometriosis

You may not qualify if:

• Expected lack of patient compliance or inability of the patient to understand the purpose of the clinical trial
• Absence of patient consent
• Malignancies

Sponsors & Collaborators

• University Hospital Tuebingen: lead

Study Sites (1)

University Hospital Tuebingen, Department of Women's Health

Tübingen, 72076, Germany

Location

MeSH Terms

Conditions

Endometriosis

Condition Hierarchy (Ancestors)

Genital Diseases, Female; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Genital Diseases

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
• Primary Completion: January 28, 2025
• Study Completion: June 30, 2025
• Last Updated: January 29, 2026

Record last verified: 2026-01

Locations

DE (1)