AI for Endometrial Cancer Detection in Post-Menopausal Bleeding
This study is looking at whether artificial intelligence (AI) can help identify endometrial cancer or pre-cancerous changes using images from a transvaginal ultrasound (an internal ultrasound). You may be able to join if you are a woman aged 55 or older who has experienced post-menopausal bleeding and has had an endometrial biopsy available from Mayo Clinic or an external institution. The study aims to see how accurate the AI algorithm is at detecting these conditions. This is an observational study, meaning researchers will collect information without giving you a specific treatment. The current recruitment status is unclear.
- Study design
- This is an observational study planning to include 300 women. It is not a randomized trial, meaning participants will not be assigned to different treatment groups.
- What's involved
- You will undergo a transvaginal ultrasound and endometrial sampling as part of your usual care. Researchers will also review your medical records.
- Compensation
- Not stated in the trial record.
- Follow-up
- The primary goal is to measure the AI algorithm's accuracy at baseline, so long-term follow-up is not specified.
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
Non-Invasive Identification of Endometrial Cancer/Endometrial Atypical Hyperplasia With an AI-Based Classifier Applied to Transvaginal Ultrasound in Patients With Post-Menopausal Bleeding
At a glance
Conditions
Where it's being run
3 sites across 3 statesStudy leadership
- Gretchen E Glaser, MD · PRINCIPAL_INVESTIGATOR · Mayo Clinic in Rochester
Who to contact
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Do you actually qualify for this trial?
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Inclusion
Exclusion
What this trial measures
- Accuracy of artificial intelligence (AI) algorithm applied to transvaginal ultrasound (TVUS)Baseline
Two static TVUS images (one longitudinal, one transversal) will be independently validated and compared to results of endometrial sampling. Outcomes from these will be compared with predictions made by the AI models for accuracy of assessing premalignant/malignant disease.