Résumé :
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« Endometriosis, characterized by endometrial-like tissue outside the uterus, is thought to affect 2-10% of women of repoductive age: representing about 190 million women worldwide. Numerous studies have evaluated the diagnostic value of blood biomarkers but with disappointing results. Thus, the gold stadard for diagnosing endometriosis remains labaroscopy. We performed a prospective trial, the ENDO-miRNA study, using both Artificial Intelligence (AI) and Machine Learning (ML), to analyze the current human miRNome to differentiate between patients with and without endometriosis, and to develop a blood-based microRNA (miRNA) dignostic signature for endometriosis. Here, we present the first blood-based diagnostic signature obtained from a combinaison of two robust and disruptive technologies merging the intrinsic quality of miRNAs to condense the endometriosis phenotype (and its heterogeneity) with the modeling power of AI. The post accurate signature provides a sensitivity, specitivity, and Area Under the Curve (AUC) of 96.8%, 100%, and 98.4%, respectively, and is sufficiently robust and reproducible to replace the gold standard of diagnostic surgery. Such a diagnostic approach for this debilitating disorder could impact recommandations from national and international learned societies. »
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