NON-INVASIVE BIOMARKERS AND ADVANCED IMAGING FOR EARLY DETECTION OF ENDOMETRIOSIS: DIAGNOSTIC PERFORMANCE, REAL-WORLD EVIDENCE, ARTIFICIAL INTELLIGENCE, AND IMPLEMENTATION PRIORITIES

Xolboyeva Mashhura
2026 · doi:10.5281/zenodo.23451118 · W7222892220
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This narrative synthesis evaluates the diagnostic performance of non-invasive endometriosis detection methods, including transvaginal ultrasonography, MRI, and biomarkers, while outlining implementation priorities supported by real-world data and artificial intelligence.

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This narrative review evaluates the diagnostic performance of non-invasive modalities, including expert transvaginal ultrasonography, magnetic resonance imaging, and saliva-based microRNA signatures, for early endometriosis detection. The authors critically appraise these tools alongside real-world data and artificial intelligence applications to support their safe clinical implementation, noting that no de novo pooled effect estimates were calculated due to the study's design. A major limitation is that this work relies on existing guidelines and systematic evidence rather than primary patient-level analysis or prospective registration. This paper is centrally about endometriosis — specifically focusing on the transition from surgical diagnosis to non-invasive detection strategies using advanced imaging and biomarkers.

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Abstract

Endometriosis is a chronic, heterogeneous inflammatory disease for which delayed recognition and unequal access to specialist imaging remain important clinical problems. The transition from surgically centered diagnosis to an integrated, non-invasive strategy has increased interest in expert transvaginal ultrasonography, magnetic resonance imaging, circulating biomarkers, and saliva-based microRNA signatures. Objective: To critically appraise the diagnostic role of these modalities and describe how real-world data, causal inference and artificial intelligence can support safe implementation. Methods: A structured critical narrative evidence synthesis was undertaken using identifiable clinical guidelines, regulatory publications, diagnosticaccuracy studies and systematic evidence available through October 2026. This is not a prospectively registered systematic review or a new patient-level analysis; no de novo pooled effect estimates were calculated.
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NON-INVASIVE BIOMARKERS AND ADVANCED IMAGING FOR EARLY DETECTION OF ENDOMETRIOSIS: DIAGNOSTIC PERFORMANCE, REAL-WORLD EVIDENCE, ARTIFICIAL INTELLIGENCE, AND IMPLEMENTATION PRIORITIES Authors/Creators Description Endometriosis is a chronic, heterogeneous inflammatory disease for which delayed recognition and unequal access to specialist imaging remain important clinical problems. The transition from surgically centered diagnosis to an integrated, non-invasive strategy has increased interest in expert transvaginal ultrasonography, magnetic resonance imaging, circulating biomarkers, and saliva-based microRNA signatures. Objective: To critically appraise the diagnostic role of these modalities and describe how real-world data, causal inference and artificial intelligence can support safe implementation. Methods: A structured critical narrative evidence synthesis was undertaken using identifiable clinical guidelines, regulatory publications, diagnosticaccuracy studies and systematic evidence available through October 2026. This is not a prospectively registered systematic review or a new patient-level analysis; no de novo pooled effect estimates were calculated. Files Xolboyeva M 222-250 ACU.pdf Additional details References - 1. World Health Organization. Endometriosis. Fact sheet. 2025. Available from: https://www.who.int/news-room/fact-sheets/detail/endometriosis - 2. Becker CM, et al. ESHRE guideline: endometriosis. Hum Reprod Open. 2022;2022(2):hoac009. Available from: https://academic.oup.com/hropen/article/2022/2/hoac009/6537540 - 3. European Society of Human Reproduction and Embryology. Endometriosis guideline. 2022. Available from: https://www.eshre.eu/Guidelines-andLegal/Guidelines/Endometriosis-guideline - 4. Validation of a Saliva Micro-RNA Signature for Endometriosis. NEJM Evid. 2025. doi:10.1056/EVIDoa2400195. Available from: https://pubmed.ncbi.nlm.nih.gov/41147827/ - 5. Transvaginal ultrasound and magnetic resonance imaging in detecting rectosigmoid deep infiltrating endometriosis: a comparative meta-analysis. 2025. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC11955469/

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