Objective: Validate diagnostic accuracy of new unique biomarker, gastrointestinal myoelectrical activity (GIMA), detected by electroviscerography (EVG) with Ai-derived disease threshold score calculation to noninvasively diagnose endometriosis. Design: Multicenter prospective blinded trial Setting: Women’s Healthcare Center Population of Sample: 165 patients with and without endometriosis diagnosis Methods: Initial 50 patients meeting inclusion criteria in 165-patient multicenter prospective GIMA biomarker trial were selected for interim analysis. Study population included women 27-55 years old, 25 with diagnosis of endometriosis and 25 non-endometriosis controls. Clinical and GIMA data were collected between February 2007 and September 2017, at all harvesting time points and frequency bands using EVG. Ai-derived threshold score calculations used area under the curve (AUC), age and standardized pain scores variables. Main Outcome Measures: Specificity, sensitivity, NPV, PPV and predictive probability or C-Statistic from logistical regression analyses of all AUC frequency and time points. Results: Non-endometriosis versus endometriosis cohort interim analysis differed significantly (p<0.001) for median (IQR), AUC values, and percent frequency power distribution at baseline, 10-minute, 20-minute, and 30-minute post water-load at frequency ranges 15-20cpm, 30-40cpm, 40-50cpm and 50-60cpm. GIMA threshold scoring revealed sensitivity and PPV of 96%, specificity and NPV of 96% and C-statistic of 100%. Ai-derived GIMA biomarkers threshold scoring predicted 25/25 subjects positive and negative for endometriosis, with surgical confirmation. Hormonal therapy, surgical stage, age nor pain score affected diagnostic accuracy. Conclusions: EVG GIMA biomarker data with Ai-derived threshold scoring accurately distinguished participants with and without endometriosis. This interim analysis supports continued investigation of GIMA biomarkers to diagnose endometriosis.