Clinical use of artificial intelligence in endometriosis: a scoping review

Brintha Sivajohan, Mohamed Elgendi, Carlo Menon, Catherine Allaire, Paul Yong, Paul J. Yong, Mohamed A Bedaiwy
NPJ digital medicine · 2022 · vol. 5(1) , pp. 109 · doi:10.1038/s41746-022-00638-1 · PMID:35927426 · PMC9352729 · W4289781034
review OA: gold CC0 ⤵ 49 in-corpus citations
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⚙ AI-generated summary by claude@2026-06, 2026-06-07 ⓘ

This scoping review of 36 studies found that artificial intelligence models, particularly logistic regression, demonstrate promising diagnostic and predictive capabilities for endometriosis, with pooled sensitivities ranging from 81.7-96.7% and specificities from 70.7-91.6%.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-07 ⓘ

This scoping review examined how artificial intelligence has been used to address clinical problems in endometriosis, including model-based prediction of outcomes, diagnostic classification, and (less commonly) disease understanding, drawing from 36 included studies identified across four databases. Most studies were retrospective and non-randomized, using heterogeneous data types (e.g., biomarkers, clinical variables, metabolite spectra, genetic variables, imaging, and lesion characteristics), with logistic regression the most common method; across studies, pooled diagnostic sensitivity ranged roughly from 81.7 to 96.7% and pooled specificity from 70.7 to 91.6%. The review’s major limitations included restricting to English-accessible articles and the absence of randomized studies, limiting certainty about clinical performance and generalizability. This paper is centrally about endometriosis — it specifically reviews the clinical use of AI methods for endometriosis diagnosis, prediction, and related research applications.

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Abstract

Endometriosis is a chronic, debilitating, gynecologic condition with a non-specific clinical presentation. Globally, patients can experience diagnostic delays of ~6 to 12 years, which significantly hinders adequate management and places a significant financial burden on patients and the healthcare system. Through artificial intelligence (AI), it is possible to create models that can extract data patterns to act as inputs for developing interventions with predictive and diagnostic accuracies that are superior to conventional methods and current tools used in standards of care. This literature review explored the use of AI methods to address different clinical problems in endometriosis. Approximately 1309 unique records were found across four databases; among those, 36 studies met the inclusion criteria. Studies were eligible if they involved an AI approach or model to explore endometriosis pathology, diagnostics, prediction, or management and if they reported evaluation metrics (sensitivity and specificity) after validating their models. Only articles accessible in English were included in this review. Logistic regression was the most popular machine learning method, followed by decision tree algorithms, random forest, and support vector machines. Approximately 44.4% (n = 16) of the studies analyzed the predictive capabilities of AI approaches in patients with endometriosis, while 47.2% (n = 17) explored diagnostic capabilities, and 8.33% (n = 3) used AI to improve disease understanding. Models were built using different data types, including biomarkers, clinical variables, metabolite spectra, genetic variables, imaging data, mixed methods, and lesion characteristics. Regardless of the AI-based endometriosis application (either diagnostic or predictive), pooled sensitivities ranged from 81.7 to 96.7%, and pooled specificities ranged between 70.7 and 91.6%. Overall, AI models displayed good diagnostic and predictive capacity in detecting endometriosis using simple classification scenarios (i.e., differentiating between cases and controls), showing promising directions for AI in assessing endometriosis in the near future. This timely review highlighted an emerging area of interest in endometriosis and AI. It also provided recommendations for future research in this field to improve the reproducibility of results and comparability between models, and further test the capacity of these models to enhance diagnosis, prediction, and management in endometriosis patients.

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Condition tags

endometriosis

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.

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