Endometriosis is a chronic condition characterized by the presence of endometrial-like glands and stroma outside the uterine cavity, leading to a range of nonspecific symptoms including dysmenorrhea, pelvic pain, dyspareunia, and/or infertility [1,2]. Despite affecting up to 10% of women of reproductive age, endometriosis remains notoriously difficult to diagnose due to its heterogeneous clinical presentation [3,4]. This, in conjunction with the need for diagnostic laparoscopy for definitive diagnosis, has led to the true prevalence remaining unknown and likely underestimated [5,6]. The reliance on surgical intervention for definitive diagnosis often results in considerable diagnostic delays up to 7 to 10 years [7]. There is a notable lack of accurate, appropriately powered, noninvasive diagnostic tests to confirm the diagnosis of endometriosis other than surgical exploration [8, 9, 10, 11, 12]. Advanced ultrasound and magnetic resonance imaging have emerged as methods for mapping deep endometriosis but still have substantial limitations in diagnosing the most common types of endometriosis (superficial disease) and advanced training is still concentrated in specialized centers [10, 11, 12]. Although biomarkers have shown promising results in small-scale studies, they currently lack the evidence needed for universal clinical implementation [8,13]. Thus, noninvasive diagnostic methods are urgently needed to facilitate early detection and management.

Prediction models for endometriosis have demonstrated acceptable performance using traditional statistical techniques [14, 15, 16, 17]. However, these models restricted the number of variables to avoid overfitting, a common limitation of classical statistical approaches [17]. Additionally, the populations studied were limited to individuals presenting with a preset duration of pelvic pain, those experiencing infertility/subfertility, or were designed to predict only site-specific endometriosis [14,16,18, 19, 20]. These limitations restrict the models’ ability to perform well in real-world settings. The rise of artificial intelligence (AI) utilization in healthcare offers the opportunity to use machine learning algorithms (MLAs) to analyze large datasets and detect patterns in presentation beyond what is evident in isolated comparisons. Other studies have utilized AI in this setting, but these models lack a rigorous delineation of cases and controls, potentially confounding the findings, and focus on subjective, patient-reported features [21,22].

Our study sought to examine the predictive value of various clinical features in the diagnosis of endometriosis by utilizing MLAs, a class of AI methods that identify patterns in data to make predictions. We hope that these findings will ultimately improve outcomes by hastening time to referral, diagnosis, and surgical intervention.