From AI to Biomarkers: How Non-Invasive Endometriosis Diagnostics Shape Women’s Quality of Life - Sochocka, Agata; Janik, Dominika; Gańska, Oliwia Zuzanna; Szepietowska, Martyna; Sikorska, Alicja; Nowicka, Nina; Zapart, Zuzanna; Parda, Julia; Bundyra, Jakub; Mian, Emil (Quality in Sport, 2026)
Background. Endometriosis is a chronic gynecological disorder that heavily impacts women’s physical and psychosocial well-being. Reliance on surgical diagnosis causes extensive delays, prolonged symptoms, and reduced quality of life. Thus, developing non-invasive diagnostic strategies has become a major research priority. Aim. To evaluate recent non-invasive diagnostic modalities for endometriosis and analyze their impact on women’s health and quality of life. Material and methods. A narrative review of English-language literature (2015–2025) was conducted using PubMed, Scopus, and Google Scholar. The focus included advanced imaging, biomarkers, genetic/molecular profiles, and artificial intelligence (AI) diagnostic tools. Results. Advances in transvaginal ultrasound and MRI have substantially improved the detection of deep infiltrating endometriosis and ovarian endometriomas. While molecular biomarkers, genetic panels, and AI show immense potential, they currently lack the precision for independent diagnosis. Clinical evidence suggests that multimodal diagnostic protocols yield the highest accuracy. Conclusions. Transitioning to non-invasive pathways can minimize diagnostic delays, reduce surgical interventions, and significantly improve quality of life. An integrated model combining various non-invasive techniques is the most effective paradigm for early endometriosis management.