Emerging Pathways to Non-Invasive Diagnosis in Endometriosis: Integrating Machine Learning, Deep Learning and Multi-Omics Biomarkers

Daniel Markov, Jasmin Gurung, Usman Khalid, Kristian Bechev, Vladimir Aleksiev, Galabin Markov, Poryazova E, Elena Poryazova
Diagnostics · 2026 · vol. 16(12) , pp. 1823 · doi:10.3390/diagnostics16121823 · PMID:42351484 · W7164755632
review OA: gold CC0
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This review analyzes how machine learning, deep learning, and multi-omics biomarkers are being integrated to develop non-invasive endometriosis diagnostics, improve accuracy, and reduce delays, despite current limitations.

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Abstract

Endometriosis is a chronic, debilitating condition affecting approximately 10-15% of reproductive-aged women and it is often associated with significant diagnostic delays due to its heterogeneity and unreliable non-invasive tests. Artificial intelligence (AI) offers innovative methods for improving endometriosis diagnosis, prognosis and research via advanced pattern recognition and data analysis capabilities. The integration of AI in diagnostic workflow has the potential to improve efficiency, accuracy, and patient outcomes. This review summarises current developments of AI-including machine learning, deep learning, and natural language processing-in the diagnostic workflow of endometriosis. It analyses different fields of diagnostics ranging from AI-assisted imaging in detection of pouch of Douglas to multi-omics biomarkers assisting the clinical decision process. AI can enhance accuracy, reducing diagnostic delays and supporting personalised treatment planning. However, there are multiple limitations, such as small datasets, overfitting, and lack of external validation and variability. Further research and evaluation are required before it can be implemented into healthcare systems. AI holds promise as a non-invasive, scalable adjunct to current diagnostics, potentially reducing the economic and personal burden endometriosis carries.

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Citation neighborhood (2-hop)

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. Outer rings show 2-hop neighbours — papers reached through the immediate citers/citees. [ collapse to 1-hop ]

References (50)

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