Fertility
Australian scientists make world-first endometriosis treatment breakthrough
The findings could help researchers determine whether a woman will need fertility treatment

A team of Australian researchers has become the first in the world to grow tissue from every known type of endometriosis, in a move that could pave the way for new research into more effective treatments and diagnoses.
Researchers from the Royal Hospital for Women, Sydney will be able to test treatments and match patients’ symptoms with specific proteins, after successfully growing 30 different lab-grown tissues taken during surgery.
The advancement will not only allow scientists to vary treatments from different types of endometriosis, but it will also help them determine whether a woman will need fertility treatment in the future.
Endometriosis is a common disease where tissue similar to the lining of the womb grows outside it in other parts of the body.
According to Endometriosis Australia, more than 830,000 of Australian girls and women live with endometriosis at some point in their life, with the disease often starting in teenagers. However, symptoms can vary dramatically between patients.
The charity estimates the illness costs the country AUS$9.7bn per year, with AUS$2.5bn coming in direct healthcare costs, and the rest in lost productivity.
Professor of obstetrics and gynaecology at Sydney Royal Hospital for Women, Jason Abbott, said collecting the cells in one place for the first time would allow researchers to accurately diagnose patients, potentially limiting the need for invasive and painful surgeries.
“Very much like we thought breast cancer, 30 years ago, was one disease and treated in one way, we now understand that [endometriosis] is much more complex than that,” he told Brisbane Times.
“All those different types of endometriosis really probably reflect different diseases … and that [can determine] their response to treatment, clinical symptoms and also things like infertility.”
The development, Abbott said, would help fast-track new treatments by enabling researchers to test new and existing drugs on the lab tissues rather than in humans.
“It’ll mean that we’ll be offering new treatments just around the corner,” he added.
Endometriosis has often been referred to as the ‘missed disease’ due to its unclear aetiology and inconsistencies in its diagnosis and management.
Unlike other long-term conditions such as diabetes and asthma, reports suggest endometriosis has remained largely ignored in government policy and research funding globally.
A 2021 study concluded that while social science and feminist research has drawn attention to the historical absences around endometriosis and the exclusion of women’s accounts, more empirical work which explores the active construction of ignorance around endometriosis in contemporary healthcare and policy contexts is urgently needed.
Fertility
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Fertility
AI could transform ovarian care through personalisation, study finds

AI could transform ovarian care by personalising cancer and fertility treatment, but more clinical validation is needed before routine use.
A systematic review and meta-analysis found AI models showed high diagnostic accuracy for ovarian cancer when combining data such as ultrasound scans and blood test results.
Across 81 studies, AI models correctly identified ovarian cancer in around nine out of 10 cases, with pooled rates of 89 to 94 per cent.
They were also highly accurate at ruling out ovarian cancer when it was not present, with specificity of 85 to 91 per cent.
The analysis also found that explainable AI tools could predict complete surgical cytoreduction in advanced ovarian cancer.
Complete surgical cytoreduction means removing all visible cancer during surgery, which can be an important goal in treatment planning.
The tools achieved a pooled AUC of 0.87. AUC is a measure of how well a model distinguishes between different outcomes, with higher scores showing stronger performance.
In reproductive medicine, AI algorithms helped physicians optimise ovarian stimulation protocols and predict follicular growth during IVF.
Ovarian stimulation is the use of hormones to encourage the ovaries to produce eggs, while follicles are the small sacs in the ovaries where eggs develop.
The review found AI could reliably model ovarian response in IVF with a pooled AUC of 0.81.
However, researchers said challenges remain in translating promising research findings into routine clinical practice.
They identified substantial variation across studies, driven by retrospective study designs, variable AI systems and a lack of standardised validation.
Only 22 per cent of analysed studies reported prospective, multicentre external validation, where models are tested forward in time across multiple healthcare settings.
The authors called for rigorous validation to help close the gap between research and routine clinical practice, alongside standardised methodological and reporting frameworks, smooth integration with clinical workflow and robust governance to support responsible and ethical AI use.
They concluded: “Artificial intelligence is a transformative force in the management of ovarian conditions.
“In gynaecologic oncology, AI enhances every phase of care, from early detection and accurate diagnosis to prognostic stratification and surgical planning.”
In reproductive medicine, AI personalises ovarian stimulation and refines the diagnosis of heterogenous endocrine disorders such as PCOS.
PCOS, or polycystic ovary syndrome, is a hormonal condition that can affect periods, skin, weight and fertility.
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