Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature Review
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 u...
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| Format: | Article |
| Language: | English |
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Nepal Medical Association
2025-02-01
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| Series: | Journal of Nepal Medical Association |
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| Online Access: | https://www.jnma.com.np/jnma/index.php/jnma/article/view/8897 |
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| author | Palpasa Shrestha Bibek Shrestha Jati Shrestha Jun Chen |
| author_facet | Palpasa Shrestha Bibek Shrestha Jati Shrestha Jun Chen |
| author_sort | Palpasa Shrestha |
| collection | DOAJ |
| description |
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.
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| format | Article |
| id | doaj-art-1c688065084a4161b7ffdc552ab44a39 |
| institution | DOAJ |
| issn | 0028-2715 1815-672X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nepal Medical Association |
| record_format | Article |
| series | Journal of Nepal Medical Association |
| spelling | doaj-art-1c688065084a4161b7ffdc552ab44a392025-08-20T03:15:38ZengNepal Medical AssociationJournal of Nepal Medical Association0028-27151815-672X2025-02-016328310.31729/jnma.8897Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature ReviewPalpasa Shrestha0Bibek Shrestha1Jati Shrestha2Jun Chen3Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, People's Republic of ChinaDepartment of Radiology, Zhongnan Hospital of Wuhan University, Hubei Province, People's Republic of ChinaNational Trauma Center, Mahankal, Kathmandu, Nepal.Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, People's Republic of China 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. https://www.jnma.com.np/jnma/index.php/jnma/article/view/8897artificial intelligencediagnostic imagingendometriosismachine learning |
| spellingShingle | Palpasa Shrestha Bibek Shrestha Jati Shrestha Jun Chen Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature Review Journal of Nepal Medical Association artificial intelligence diagnostic imaging endometriosis machine learning |
| title | Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature Review |
| title_full | Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature Review |
| title_fullStr | Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature Review |
| title_full_unstemmed | Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature Review |
| title_short | Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature Review |
| title_sort | current status and future potential of machine learning in diagnostic imaging of endometriosis a literature review |
| topic | artificial intelligence diagnostic imaging endometriosis machine learning |
| url | https://www.jnma.com.np/jnma/index.php/jnma/article/view/8897 |
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