EndometriosisNCT06873373
AI Based Real Time Detection of Endometriosis Lesions
An observational study of Endometriosis, run by University Hospital Tuebingen at 1 site in Germany.
Trial summary
Development of AI-based approaches for automated real-time detection of endometriosis lesions using endoscopic image and video material. Read more about this study
- Who is it for?
- Ages 18 years and older, women only
- Conditions the study is for
- Endometriosis
- How many people
- 26 participants
- Where
- 1 siteGermany
- Study dates
- 10 Oct 2023to 30 Jun 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Women only
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- Age ≥ 18 years
- Written consent after explanation
- Indication for surgical treatment of endometriosis
Who cannot join
- Expected lack of patient compliance or inability of the patient to understand the purpose of the clinical trial
- Absence of patient consent
- Malignancies
Trial locations and contacts
1 site in 1 country.
Germany1 site
University Hospital Tuebingen, Department of Women's Health
Tübingen, 72076
About this study
Official title: Development of AI-Based Approaches for Automated Real-Time Detection of Endometriosis Lesions Using Endoscopic Image and Video Data
Development of AI-based approaches for automated real-time detection of endometriosis lesions using endoscopic image and video material.
Read the detailed description
In the field of endometriosis, artificial intelligence (AI) has been used for diagnoses or even predictions of endometriosis before confirmation through laparoscopy. AI's significant potential in minimally invasive surgery lies in automatic image analysis, aiding in the detection of structures or anomalies based on image data. This offers the potential to detect endometriosis lesions during laparoscopy regardless of the indication. Training and creating such AI models are done using machine learning algorithms based on annotated data. These training data consist of image data with pixel-level annotations of the content that the model should detect. Deep learning (DL) algorithms have proven effective in image analysis, relying on neural networks to autonomously fill them with the most critical decision criteria for correct analysis of the image content. The trained AI model can then be applied to unknown data, providing the probability of detecting a structure for each pixel. Possible visual outputs of the model include outlining the detected content or segmenting, assigning predefined content to each pixel. The quality of the model depends crucially on a sufficiently large number and quality of training data. Quality includes correct annotation of data to prevent the model from learning errors. Diversifying image data by including negative examples in the training and test datasets is equally important. The F1-score is used as a measure of the model's quality, combining precision (P) with recall (R) to a value between 0 and 1, based on an annotated test dataset.
The goal is to achieve a high F1-score through the selection of training data and an appropriate DL algorithm. Parameters like image preparation optimization or DL algorithm parameters such as selecting different neural networks can improve the F1-score. The number of required training data for a good AI model depends on the complexity of the question and the number of contents to be detected, as the model can only recognize learned content. It is possible to iteratively adjust the selection of training data for different questions based on the achieved F1-scores after each training and testing. If necessary, the number of training data can be increased, and problematic image data, such as missing annotations, can be identified and corrected based on the results.
- Conditions
- Endometriosis
- Medical subject headings
- Endometriosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Suspected endometriosis | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
Measured: 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.
Measured: During time-span of study (approx. 1 year)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Mar 2025
- Study start10 Oct 2023
- Primary completion28 Jan 2025
- Study completion30 Jun 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for EndometriosisStudies of the same condition, open and under way.
- More trials run by University Hospital TuebingenThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 26 participants (actual)
Sponsor and investigators
- Lead sponsor
- University Hospital Tuebingen
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06873373
- Sponsor's protocol ID
- MT_STORZ federated learning
- Status last verified
- Jan 2026
- First submitted
- 17 Jan 2025
- Last updated
- 29 Jan 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 29 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of AI-Based Approaches for Automated Real-Time Detection of Endometriosis Lesions Using Endoscopic Image and Video Data
- ClinicalTrials.gov ID
- NCT06873373
- Acronym / sponsor ID
- MT_STORZ federated learning
- Conditions
- Endometriosis
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 26 participants (actual)
- Lead sponsor
- University Hospital TuebingenUniversity, hospital or other
- Study start
- 10 Oct 2023 (actual)
- Primary completion
- 28 Jan 2025 (actual)
- Study completion
- 30 Jun 2025 (actual)
- First posted
- 12 Mar 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Development of AI-based approaches for automated real-time detection of endometriosis lesions using endoscopic image and video material.
Detailed description
In the field of endometriosis, artificial intelligence (AI) has been used for diagnoses or even predictions of endometriosis before confirmation through laparoscopy. AI's significant potential in minimally invasive surgery lies in automatic image analysis, aiding in the detection of structures or anomalies based on image data. This offers the potential to detect endometriosis lesions during laparoscopy regardless of the indication. Training and creating such AI models are done using machine learning algorithms based on annotated data. These training data consist of image data with pixel-level annotations of the content that the model should detect. Deep learning (DL) algorithms have proven effective in image analysis, relying on neural networks to autonomously fill them with the most critical decision criteria for correct analysis of the image content. The trained AI model can then be applied to unknown data, providing the probability of detecting a structure for each pixel. Possible visual outputs of the model include outlining the detected content or segmenting, assigning predefined content to each pixel. The quality of the model depends crucially on a sufficiently large number and quality of training data. Quality includes correct annotation of data to prevent the model from learning errors. Diversifying image data by including negative examples in the training and test datasets is equally important. The F1-score is used as a measure of the model's quality, combining precision (P) with recall (R) to a value between 0 and 1, based on an annotated test dataset.
The goal is to achieve a high F1-score through the selection of training data and an appropriate DL algorithm. Parameters like image preparation optimization or DL algorithm parameters such as selecting different neural networks can improve the F1-score. The number of required training data for a good AI model depends on the complexity of the question and the number of contents to be detected, as the model can only recognize learned content. It is possible to iteratively adjust the selection of training data for different questions based on the achieved F1-scores after each training and testing. If necessary, the number of training data can be increased, and problematic image data, such as missing annotations, can be identified and corrected based on the results.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Suspected endometriosis | – | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- Age ≥ 18 years
- Written consent after explanation
- Indication for surgical treatment of endometriosis
Key exclusion criteria (3 listed)
- Expected lack of patient compliance or inability of the patient to understand the purpose of the clinical trial
- Absence of patient consent
- Malignancies
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Development and validation of an AI model for real-time automated detection of endometriosis lesions
Time frame: During time-span of study (approx. 1 year)
* Based on laparoscopic image and video data. * Evaluation of model accuracy using the F1-score, with a target value of ≥ 0.7.
Quality of video anonymization
Time frame: During time-span of study (approx. 1 year)
* 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
Creation of a high-quality annotated image dataset for AI training
Time frame: During time-span of study (approx. 1 year)
* 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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Jan 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Endometriosis, run by University Hospital Tuebingen at 1 site in Germany.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 29 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Germany. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Development and validation of an AI model for real-time automated detection of endometriosis lesions (time frame: During time-span of study (approx. 1 year)). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Jan 2026.