Noninvasive diagnostic imaging for endometriosis part 1: a systematic review of recent developments in ultrasound, combination imaging, and artificial intelligence

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dc.contributor.authorAvery, J.C.
dc.contributor.authorDeslandes, A.
dc.contributor.authorFreger, S.M.
dc.contributor.authorLeonardi, M.
dc.contributor.authorLo, G.
dc.contributor.authorCarneiro, G.
dc.contributor.authorCondous, G.
dc.contributor.authorHull, M.L.
dc.contributor.authorImagendo Study Group,
dc.date.issued2024
dc.description.abstractEndometriosis affects 1 in 9 women and those assigned female at birth. However, it takes 6.4 years to diagnose using the conventional standard of laparoscopy. Non-invasive imaging enables a timelier diagnosis, reducing diagnostic delay as well as the risk and expense of surgery. This review updates the exponentially increasing literature exploring the diagnostic value of endometriosis specialist transvaginal ultrasound (eTVUS), combinations of eTVUS and specialist magnetic resonance imaging (eMRI), and Artificial Intelligence (AI). Concentrating on literature that emerged after publication of the IDEA consensus in 2016, we identified 6192 publications, and reviewed 49 studies focused on diagnosing endometriosis using emerging imaging techniques. The diagnostic performance of eTVUS continues to improve but there are still limitations. eTVUS reliably detects OE and shows high specificity for deep endometriosis (DE) and should be considered diagnostic. However, a negative scan cannot preclude endometriosis as eTVUS shows moderate sensitivity scores for DE and the sonographic evaluation of superficial endometriosis is still in its infancy. The fast growing area of AI in endometriosis detection is still evolving, but shows great promise, particularly in the area of combined multimodal techniques. We finalise our commentary by exploring the implications for practice change for surgeons, sonographers, radiologists, and fertility specialists. Direct benefits for endometriosis patients include reduced diagnostic delay, better access to targeted therapeutics, higher quality operative procedures and improved fertility treatment plans.
dc.description.statementofresponsibilityJodie C. Avery, Alison Deslandes, Shay M. Freger, Mathew Leonardi, Glen Lo, Gustavo Carneiro, G. Condous, Mary Louise Hull, and the Imagendo Study Group
dc.identifier.citationFertility and Sterility, 2024; 121(2):164-188
dc.identifier.doi10.1016/j.fertnstert.2023.12.008
dc.identifier.issn0015-0282
dc.identifier.issn1556-5653
dc.identifier.orcidAvery, J.C. [0000-0002-8857-9162]
dc.identifier.orcidDeslandes, A. [0000-0001-7094-3950]
dc.identifier.orcidLeonardi, M. [0000-0001-5538-6906]
dc.identifier.orcidCarneiro, G. [0000-0002-5571-6220]
dc.identifier.urihttps://hdl.handle.net/2440/141124
dc.language.isoen
dc.publisherElsevier
dc.relation.grantARC
dc.rightsCrown Copyright ©2023 Published by Elsevier Inc. on behalf of the American Society for Reproductive Medicine
dc.source.urihttp://dx.doi.org/10.1016/j.fertnstert.2023.12.008
dc.subjectArtificial Intelligence
dc.subjectCombination Imaging
dc.subjectDiagnosis
dc.subjectEndometriosis
dc.subjectUltrasound
dc.titleNoninvasive diagnostic imaging for endometriosis part 1: a systematic review of recent developments in ultrasound, combination imaging, and artificial intelligence
dc.typeJournal article
pubs.publication-statusPublished

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