EARLY DETECTION OF ENDOMETRIOSIS – A LITERATURE REVIEW OF DIAGNOSTIC METHODS AND FUTURE PERSPECTIVES

In: International Journal of Innovative Technologies in Social Science · 2026 · vol. 4(3(51)) · doi:10.31435/ijitss.3(51).2026.6129 · W7213645724
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This literature review evaluates current and emerging diagnostic methods for endometriosis, concluding that imaging techniques form the foundation of diagnosis while biomarkers lack sufficient accuracy and AI-based models show promising potential.

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This literature review evaluates current and emerging diagnostic methods for endometriosis, focusing on imaging techniques, biomarkers, and artificial intelligence applications. The authors identify transvaginal ultrasound as the primary first-line imaging modality, with magnetic resonance imaging serving as a complementary tool for staging complex cases or assessing bowel involvement. While numerous biomarkers have been investigated, none currently demonstrate sufficient accuracy for routine clinical use, and emerging technologies like AI require further validation before widespread adoption. This paper is centrally about endometriosis — specifically reviewing non-invasive diagnostic strategies to improve early detection and reduce diagnostic delays.

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Abstract

Research objectives: Endometriosis is a chronic disease that impairs the daily functioning and quality of life of many women. This literature review summarizes current diagnostic methods for endometriosis, their advantages and limitations, and discusses emerging approaches, particularly established and emerging imaging techniques. Scope of review: Relevant publications were identified through searches of PubMed, Scopus, Web of Science, Google Scholar, and other scientific databases. This literature review focused on studies published between 2020 and 2025 that addressed established and emerging diagnostic approaches for endometriosis, including imaging techniques, biomarkers, and artificial intelligence. Findings: Endometriosis commonly presents with chronic pelvic pain, dysmenorrhea, dyspareunia, and infertility. Current evidence indicates that imaging plays a central role in diagnosis. Transvaginal ultrasound remains the first-line imaging modality, while magnetic resonance imaging provides complementary information for disease staging and assessment of complex cases. Transrectal ultrasound may be useful in selected patients with suspected bowel involvement. Although numerous biomarkers have been investigated, none have demonstrated sufficient diagnostic accuracy for routine clinical use. Emerging technologies, including advanced imaging techniques and AI-based diagnostic models, show promising potential but still require further validation before clinical use. Conclusions: Early diagnosis remains essential for improving patient outcomes and limiting disease progression. However, the disease is often diagnosed too late, which remains a major clinical challenge. Recent evidence supports imaging-based diagnostic strategies as the foundation of diagnosis, whereas biomarker-based approaches require further development. Future advances in AI and integrated diagnostic models may contribute to more accurate and timely detection of endometriosis.
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Conclusions

Early diagnosis remains essential for improving patient outcomes and limiting disease progression. However, the disease is often diagnosed too late, which remains a major clinical challenge. Recent evidence supports imaging-based diagnostic strategies as the foundation of diagnosis, whereas biomarker-based approaches require further development. Future advances in AI and integrated diagnostic models may contribute to more accurate and timely detection of endometriosis.

References

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The benefit of transvaginal elastography in detecting deep endometriosis: A feasibility study. Ultraschall Med, 45(1), 69–76. https://doi.org/10.1055/a-2028-8214 Brunelli, A. C., Brito, L. G. O., Moro, F. A. S., Jales, R. M., Yela, D. A., & Benetti-Pinto, C. L. (2023). Ultrasound elastography for the diagnosis of endometriosis and adenomyosis: A systematic review with meta-analysis. 49(3), 699–709. https://doi.org/10.1016/j.ultrasmedbio.2022.11.006 Downloads Published Issue Section License Copyright (c) 2026 Izabela Harpula, Natalia Bębenek, Benedykt Baljon, Justyna Bogdan, Radosław Ramotowski , Patryk Brzezicki, Paulina Frączkiewicz , Gabriela Barszcz , Monika Błądek, Karolina Karolina, Sylwia Wit This work is licensed under a Creative Commons Attribution 4.0 International License. All articles are published in open-access and licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Hence, authors retain copyright to the content of the articles. 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