Abstract
Endometriosis affects a substantial proportion of reproductive-age women yet is diagnosed, on average, only after years of symptom evaluation, since definitive confirmation still relies on surgical visualisation and histopathology rather than any single reliable non-invasive test. A retrospective case-control study analysing more than two hundred clinical features, including demographic factors, presenting symptoms, gynaecological and obstetric history, and physical examination findings collected from patients undergoing laparoscopic or robot-assisted excision, used machine learning algorithms to build an explainable preoperative prediction model intended to flag women likely to have surgically confirmed disease before they ever reach the operating theatre [1].
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