Aiming to bridge skills gap in endometriosis ultrasound

This month marks an important step forward for women’s health, with ultrasound for complex gynaecological conditions - such as endometriosis - now added to the Medicare Benefits Schedule (MBS) in Australia.

The inclusion recognises the vital role that high-quality imaging plays in diagnosing and managing conditions like endometriosis, which cause chronic pain, infertility, and significant impacts on quality of life.

While the new MBS item improves access for patients, it also highlights a challenge for the healthcare system where there is currently a shortage of sonographers with the specialised skillset required to perform these complex scans. Detecting deep endometriosis and other intricate pelvic pathologies demands advanced technical skill and anatomical knowledge - skills that many clinicians have had limited opportunity to develop.

Researchers from the Endometriosis group at Robinson Research Institute (RRI) at the University of Adelaide are aiming to bridge this gap through IMAGENDO — a multidisciplinary collaboration combining medical imaging, artificial intelligence (AI), and clinical expertise to improve endometriosis diagnosis. As part of this work, the team is developing AIMEE (AI Mentor for Endometriosis Ultrasound Education), an innovative AI-powered app designed to help sonographers learn to identify and interpret endometriosis on ultrasound images.

Led by Alison Deslandes, Specialist Sonographer and PhD student with the IMAGENDO project, AIMEE delivers a staged e-learning program to sonographers, enhanced by real-time AI feedback. The app analyses scans as they are performed, providing immediate guidance to help users refine technique, improve accuracy, and build diagnostic confidence.

Robinson Research Institute has provided STEP Research Funding in 2024 and 2025 to support the development of AIMEE - reinforcing our commitment to accelerating research translation and improving clinical practice. By upskilling sonographers, AIMEE, currently a prototype, might soon fill a critical workforce gap and ensure that people experiencing endometriosis symptoms can access earlier and more accurate diagnoses – maximising the benefits of the new MBS listing. Importantly, AIMEE also contributes to IMAGENDO’s broader goal to reduce the time to diagnose endometriosis from the current average of 6.5 years to one year by 2030.

The IMAGENDO team, led by Professor Louise Hull and Dr Jodie Avery, continues to demonstrate how AI and imaging can revolutionise diagnosis and improve outcomes for people living with endometriosis. AIMEE is the next step in ensuring that clinicians and sonographers are equipped to make the most of new technologies and policy advancements.