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Promising AI tool to speed up endometriosis diagnosis (opens in a new tab)
news-medical.net · 2026-09-16
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Promising AI tool to speed up endometriosis diagnosis
news-medical.net · 2026-09-16
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https://adelaide.edu.au/about/news/2026/promising-ai-tool-to-speed-up-endometriosis-diagnosis/
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Claim 1 of 5Not checkedAn emerging AI tool called EndoFusion can detect signs of endometriosis through one simple scan in less than a second, much faster than the current seven-year wait for surgery and less invasively.View evidenceHide evidence
As statedless than a second; current seven-year wait
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NewsLink could not identify one source study, so this claim was not compared against a paper.
Claim 2 of 5Not checkedThe tool is described as a recent development from IMAGENDO, an ongoing collaborative study led by Adelaide University researchers, and the latest study found it could accurately identify two major indicators of advanced endometriosis in pelvic scans, producing results in 18 milliseconds.View evidenceHide evidence
As stated18 milliseconds
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NewsLink could not identify one source study, so this claim was not compared against a paper.
Claim 3 of 5Not checkedResearchers said the framework provided a correct diagnosis 83% of the time, which they said was more accurate than all competing models.View evidenceHide evidence
As stated83%
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Claim 4 of 5Not checkedThe AI framework was trained using information from four datasets containing more than 9,000 female pelvic MRI scans and more than 800 transvaginal ultrasound sliding scans.View evidenceHide evidence
As statedmore than 9,000 MRI scans; more than 800 ultrasound sliding scans
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Claim 5 of 5Not checkedThe story says the work was recently published in Artificial Intelligence in Medicine and that the next step is expanding the dataset to include additional endometriosis markers to improve classification accuracy.View evidenceHide evidence
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Method layer
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Nearby research
No exact source, but these papers are close.
PubMed, Crossref, Europe PMC · 15 candidate papers
TongueNet-GYN: a multimodal deep learning framework for non-invasive gynecological disease screening in digital public health.
Frontiers in Public Health · 2026 · PubMed
Accuracy of Physical Examination, Transvaginal Sonography, Magnetic Resonance Imaging, and Rectal Endoscopic Sonography for Preoperative Evaluation of Rectovaginal Endometriosis
Ultrasound Quarterly · 2019 · Crossref
Radiomics and Artificial Intelligence in Ovarian Endometriosis Imaging: A Systematic Review and Critical Appraisal of Emerging Evidence.
Bioengineering (Basel, Switzerland) · 2026 · PubMed, Europe PMC
An ensemble machine learning model based on magnetic resonance imaging features for diagnosing deep infiltrating endometriosis.
Frontiers in Physiology · 2026 · PubMed, Europe PMC
The Diagnostic Accuracy of Magnetic Resonance Imaging Versus Transvaginal Ultrasound in Deep Infiltrating Endometriosis and Their Impact on Surgical Decision-Making: A Systematic Review
Diagnostics · 2025 · Crossref
And 10 more candidates considered.