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

Palpasa Shrestha, Bibek Shrestha, Jati Sherestha, Jun Chen
JNMA; journal of the Nepal Medical Association · 2025 · vol. 63(283) , pp. 205–211 · doi:10.31729/jnma.8897 · PMID:40656926 · W4408069459
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⚙ AI-generated summary by claude@2026-06, 2026-06-07 ⓘ

This literature review examines machine learning's current applications and future prospects for analyzing diagnostic imaging of endometriosis, a condition typically diagnosed via laparoscopy.

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Abstract

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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Condition tags

mesh:D004715endometriosis

MeSH descriptors

Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Machine Learning Machine Learning

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

References (36)

Cited by (3)

Source provenance

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