Bridging the Diagnostic Gap: Reviewing Current Endometriosis Screening Tools and Models

Fatima Kathrada, Armorel D. Van Eyk, Armorel Van Eyk, Van Eyk A, Chauke L, Lawrence Chauke
Journal of women's health (2002) · 2026 · pp. 15409996261478005 · doi:10.1177/15409996261478005 · PMID:42576664 · W7202200014
Other OA: closed public-domain-us
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This scoping review synthesizes current endometriosis screening tools, including AI-driven models and questionnaires, revealing high accuracy metrics but significant gaps in external validation and comprehensive psychometric evaluation.

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Abstract

Background: Endometriosis affects 10% of reproductive-aged women globally and is one of the major causes of infertility. It is a debilitating, chronic condition with an average diagnostic delay of up to 10 years. This is partly due to the lack of noninvasive tools to diagnose the disease, leading to disease progression and poor health-related outcomes.Aim: The purpose of this scoping review was to synthesize the available literature on the existing tools, their use and applicability, and identify current gaps in the field.Method: A comprehensive search was conducted to identify applicable studies using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews guidelines.Results: Eighteen studies were included in the review, with screening tools classified into four categories: questionnaire-based, app-based, machine learning and artificial intelligence (AI)-driven, and subtype-focused models. Among the included study tools, 14 reported on psychometric parameters with area under the receiver operating characteristic curve values ranging from 0.77 to 0.95, with machine learning and AI-driven models reporting the highest accuracy metrics.Conclusion: Significant gaps were noted across identified tools, particularly in external validation, comprehensive psychometric evaluation, and cultural adaptation.

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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 ]

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