02184nas a2200325   4500000000100000000000100001008004100002260001500043653002700058653001600085653003000101653002100131653002300152100002400175700001600199700002100215700001600236700002200252700002200274700002100296700001800317700001600335700001700351245009000368856005500458300000800513490000700521520131600528022001401844       2022                            d  c2022-01-1210aBiomedical Engineering10aEngineering10aMathematics and computing10amedical research10aSigns and symptoms1 aSofiane Bendifallah1 aAnne Puchar1 aStéphane Suisse1 aLéa Delbos1 aMathieu Poilblanc1 aPhilippe Descamps1 aFrancois Golfier1 aCyril Touboul1 aYohann Dabi1 aEmile Daraï00aMachine learning algorithms as new screening approach for patients with endometriosis  uhttps://www.nature.com/articles/s41598-021-04637-2  a6390 v123 aEndometriosis—a systemic and chronic condition occurring in women of childbearing age—is a highly enigmatic disease with unresolved questions. While multiple biomarkers, genomic analysis, questionnaires, and imaging techniques have been advocated as screening and triage tests for endometriosis to replace diagnostic laparoscopy, none have been implemented routinely in clinical practice. We investigated the use of machine learning algorithms (MLA) in the diagnosis and screening of endometriosis based on 16 key clinical and patient-based symptom features. The sensitivity, specificity, F1-score and AUCs of the MLA to diagnose endometriosis in the training and validation sets varied from 0.82 to 1, 0–0.8, 0–0.88, 0.5–0.89, and from 0.91 to 0.95, 0.66–0.92, 0.77–0.92, respectively. Our data suggest that MLA could be a promising screening test for general practitioners, gynecologists, and other front-line health care providers. Introducing MLA in this setting represents a paradigm change in clinical practice as it could replace diagnostic laparoscopy. Furthermore, this patient-based screening tool empowers patients with endometriosis to self-identify potential symptoms and initiate dialogue with physicians about diagnosis and treatment, and hence contribute to shared decision making.  a2045-2322