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Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature Review

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creativeworkseries.issnJNMA Print ISSN: 0028-2715; Online ISSN: 1815-672X
dc.contributor.authorShrestha, Palpasa
dc.contributor.authorShrestha, Bibek
dc.contributor.authorShrestha, Jati
dc.contributor.authorChen, Jun
dc.date.accessioned2025-07-28T10:53:40Z
dc.date.available2025-07-28T10:53:40Z
dc.date.issued2025
dc.descriptionPalpasa Shrestha Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, People's Republic of China Bibek Shrestha Department of Radiology, Zhongnan Hospital of Wuhan University, Hubei Province, People's Republic of China Jati Shrestha National Trauma Center, Mahankal, Kathmandu, Nepal. Jun Chen Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, People's Republic of China
dc.description.abstractAbstract The presence of endometrial tissue outside the uterus is a defining characteristic of endometriosis, a chronic systemic illness that affects women of childbearing age. Despite its enigmatic nature, laparoscopy remains the gold standard for diagnosis, while noninvasive methods such as transvaginal ultrasonography and magnetic resonance imaging are commonly used to aid in preoperative planning. In healthcare, AI has emerged as a game-changing innovation, enhancing patient outcomes, reducing costs, and revolutionizing healthcare delivery, particularly in diagnostic radiology. Images can be analyzed using machine learning, a pattern recognition method. The machine learning algorithm first computes the image characteristics deemed significant for making predictions or diagnoses about unseen images.
dc.identifierhttps://doi.org/10.31729/jnma.8897
dc.identifier.urihttps://hdl.handle.net/20.500.14572/870
dc.language.isoen_US
dc.publisherNepal Medical Association
dc.titleCurrent Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature Review
dc.typeArticle
dspace.entity.typePublication
local.article.typeReview Article
oaire.citation.endPage211
oaire.citation.startPage205
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relation.isJournalIssueOfPublication.latestForDiscovery56cb5347-de36-4b20-a0fb-1407f3cfcf82
relation.isJournalOfPublicatione6e146a0-0ece-4aba-aa0a-6ccfbd10a12a

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