What is Endometriosis?
Endometriosis is a common, chronic, inflammatory disease, causing period pain for 1 in 9 reproductive aged women, where tissue similar to the endometrium grows outside the uterus. Endometriosis is usually under diagnosed with an average of 6.4 years from first symptoms to diagnosis. Reproductive hormonal changes each month drive an excruciating cycle of endometriosis breakdown and re-modelling. If undiagnosed, chronic pelvic pain, sexual pain, fatigue, anxiety, and depression may result. Endometriosis also increases the risk of infertility, autoimmune conditions, cancer, and heart disease.
What are the potential benefits of the research project?
We may find information on the ultrasound or MRI scans that will help your surgeon better plan your surgery. However, we cannot guarantee that taking part in the trial will be of direct benefit to you. This research aims to further medical knowledge and enable new pathways to investigate and diagnose endometriosis which may help other women in the future.
You will not be paid for participation in this study and nor will you incur any financial cost. No individual researcher will gain direct financial benefit from conducting the study.
What am I being invited to do (Stage One)?
Many surgeons currently use MRI or TVUS scans as part of their pre-operative assessment for endometriosis. We are asking your permission to obtain a copy of these scans and reports for our research. Additionally, the researchers will record some questions about your symptoms and the outcome of your surgery.
Do I have to have endometriosis to be involved?
No. You just need to have previously had a pelvic magnetic resonance imaging scan (MRI) or a transvaginal ultrasound scan (TVUS), or both, for suspected endometriosis (Stage One). You can also be planning surgery for pelvic pain or endometriosis (Stage Two).
Do I have to sign both consent forms?
What will happen to my information?
Your information will only be used as described in this participant information sheet and it will only be disclosed according to the consent provided, except as required by law.
Can I withdraw from the project?
Participation in this project is completely voluntary. If you agree to participate, you can withdraw from the study up until a month after you have consented for us to access your MRI or TVUS scan. However, it will not be possible to withdraw once analysis of the study results has begun or been published.
What if I have a complaint?
The study has been approved by the Human Research Ethics Committee at the University of Adelaide (approval number H-2020-051). This research project will be conducted according to the NHMRC National Statement on Ethical Conduct in Human Research 2007 (Updated 2018). If you have questions or problems associated with the practical aspects of your participation in the project or wish to raise a concern or complaint about the project, then you should consult the Principal Investigator.
If you wish to speak with an independent person regarding concerns or a complaint, the University’s policy on research involving human participants, or your rights as a participant, please contact the:
Human Research Ethics Committee’s Secretariat on:
Phone: +61 8 8313 6028
Address: Level 4,
Rundle Mall Plaza,
50 Rundle Mall,
ADELAIDE SA 5000
Any complaint or concern will be treated in confidence and fully investigated. You will be informed of the outcome.
Who is sponsoring this study?
· 2020: Endometriosis Australia Endometriosis Research Grant: “Non-invasive
endometriosis diagnosis using machine learning” ($30,000)
· 2020: AGES Clinical Research Fund: Diagnosing endometriosis noninvasively using imaging technologies and machine learning ($9,570)
· 2021: Medical Research Future Fund’s Primary Health Care Research Data Infrastructure Grant “IMAGENDO Diagnosing endometriosis with imaging and artificial intelligence” ($1,990,998)
· 2021: Emerging Leader Development Award, Faculty of Health and Medical Science UofA ($40,000) Dr J Avery
· 2022: Australasian Society for Ultrasound in Medicine (ASUM) “Rectal deep
endometriosis identification using Ultrasound and Artificial Intelligence”