Promising AI tool to speed up endometriosis diagnosis
· Medical Xpressedited by Swati Mestri, reviewed by Andrew Zinin
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The AI tool, called "EndoFusion," is a recent development from IMAGENDOÒ, an ongoing collaborative study led by Adelaide University researchers. In this latest study, researchers found the framework could accurately identify two major indicators of advanced endometriosis in pelvic scans, producing results in just 18 milliseconds.
Researchers say this is a significant development because MRI and ultrasound imaging are often better at detecting one sign of the condition over the other.
"Current scanning methods each have their own strengths when it comes to detecting two common markers that indicate the likelihood of endometriosis, and patients will often only have access to one of them," said study author Associate Professor Jodie Avery, research co-lead of chronic reproductive conditions in the Endometriosis Research Group at Adelaide University's Robinson Research Institute.
"This means that some patients could be disadvantaged if they are scanned by the less optimal option for their particular signs. Some of the imaging tools also rely on operator experience and can be costly.
"Our AI tool can help address these shortcomings by combining data from both imaging tools, giving the framework the knowledge it needs to detect both signs of endometriosis through a single scan more effectively and efficiently."
The AI framework is still in the early stages of development. It works by using information gathered from four data sets containing more than 9,000 female pelvic MRI scans and more than 800 transvaginal ultrasound sliding scans.
"We looked at how well 'EndoFusion' was able to distinguish between positive and negative cases of endometriosis and found it was able to provide a correct diagnosis 83% of the time, which is more accurate than any competing model," said lead author Dr. Yuan Zhang from Adelaide University's Robinson Research Institute and the Australian Institute for Machine Learning.
"This is a positive step forward and moves us closer to a future where an AI tool can help clinicians provide a faster, more accurate diagnosis without the need for surgery."
More than 190 million women worldwide have endometriosis. The chronic condition occurs when uterine tissue grows outside the uterus, causing symptoms including abdominal pain, heavy periods, bloating, anxiety, fatigue and infertility.
It is notoriously difficult to diagnose and often relies on identifying lesions visually through surgery, a process that is slow, costly and risky.
"The development of accurate, noninvasive early diagnostic methods is critical to shorten the diagnostic timeline and reduce associated costs," Avery said.
Researchers collaborated with Flinders University, Benson Radiology, Omni Ultrasound and Gynaecological Care, the University of Surrey, McMaster University Medical Centre and Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).
The results were recently published in Artificial Intelligence in Medicine.
The next step will involve expanding the data set to include additional endometriosis markers to improve the tool's classification accuracy.
"We envisage that clinicians will be able to use these tools to help make a determination about the presence of endometriosis from a single scan," Zhang said.
"It could also potentially provide insights for research into other diseases that require the use of multimodal imaging, such as gynecological disorders, prostate and breast cancer, and fetal abnormalities."
More information
Yuan Zhang et al, Unpaired multi-modal multi-label learning for detecting endometriosis signs, Artificial Intelligence in Medicine (2026). DOI: 10.1016/j.artmed.2026.103503
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Obstetrics & gynecologyWomen's healthReproductive healthDiagnostic radiology Provided by University of Adelaide Who's behind this story?
Swati Mestri
Swati Mestri holds a bachelor's degree in Electronics Engineering and has worked as a content editor since 2019. She has experience editing research documents across technology, health care, and materials science, and has a particular interest in technology and space. Full profile →
Andrew Zinin
Master's in physics with research experience. Long-time science news enthusiast. Plays key role in Science X's editorial success. Full profile →
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