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

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.