EARLY DETECTION OF ENDOMETRIOSIS – A LITERATURE REVIEW OF DIAGNOSTIC METHODS AND FUTURE PERSPECTIVES
DOI:
https://doi.org/10.31435/ijitss.3(51).2026.6129Keywords:
Endometriosis, Diagnostic Methods, Transvaginal Ultrasound, Magnetic Resonance Imaging, Biomarkers, Imaging-Based Techniques, Artificial IntelligenceAbstract
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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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
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