Non-Invasive diagnosis of Endometriosis with imaging and artificial intelligence
Endometriosis is a common condition. By the age of 44, one in nine Australian women are diagnosed with endometriosis, a condition that caused 34,000 hospitalisations in 2016/17. Diagnosis is often delayed, with an average of 7-12 years between onset of symptoms and diagnosis.
Currently, the recommended way of diagnosing endometriosis is to perform keyhole surgery and visualise the endometrial deposits inside the abdomen, ideally verified by microscopic examination of the tissue. This method is recommended for the diagnosis of endometriosis but surgery can be problematic, difficult to access, and is associated with delays.
The Imagendo study will use machine learning to automatically digitally combine the diagnostic capabilities of pelvic scans and magnetic resonance imaging (MRI) to identify endometriosis lesions. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
the Imagendo Research Team
Meet our Team
No. You may have previously had a pelvic magnetic resonance imaging scan (MRI) or a transvaginal ultrasound scan (TV-US), or both in the past for any pelvic symptoms.
You can also be planning investigations or surgery for pelvic pain or Endometriosis.