Using a Deep Neural Network to Help Detect Endometriosis in Women Aged 14 to 50 with Chronic Pelvic Pain

What this Trial Is About

Researchers are evaluating the clinical validity of a deep neural network algorithm called EndoCheck to assist in diagnosing endometriosis in women who have chronic pelvic pain. This observational study aims to compare the accuracy of EndoChecks protein biomarker detection with laparoscopic surgical assessment. The studys goal is to achieve at least 94% sensitivity and 79% specificity in detecting endometriosis, with additional evaluation based on pain severity and other clinical factors. This study involves no treatment or intervention but observes participants scheduled for laparotomy or laparoscopy due to symptoms suggesting endometriosis. EndoChecks performance as a diagnostic aid will be assessed by analyzing protein biomarkers through the neural network algorithm. The study will take place over 24 months, focusing on sensitivity, specificity, and overall test performance. Participants will undergo laparoscopic or laparotomy procedures as part of their clinical care. Researchers will collect data to measure the tests accuracy in diagnosing endometriosis and observe how the test performs across different patient groups. The study monitors participants for two years, evaluating primary outcomes of sensitivity and specificity and secondary outcomes related to test performance, with no intervention beyond standard surgical assessment.

Deep Neural Network Stratification for Use Detecting Endometriosis in Women Affected by Chronic Pelvic Pain (EndoCheck)