Abstract
BACKGROUND: Endometriosis diagnosis is challenging due to non-specific symptoms that overlap with other gynaecological conditions. This study proposes a non-invasive Machine Learning (ML) ‒ based urine test using Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy for rapid, high-throughput screening. METHODS: A total of 302 symptomatic patients presenting with pelvic pain and MRI referral indications were recruited. After applying exclusion criteria, 100 patients (50 endometriosis-positive, 50 endometriosis-negative with other gynaecological conditions) were included. Urine samples were self-collected during the first visit and analysed via ATR-FTIR spectroscopy. Two Machine Learning (ML) algorithms, sensitivity-tuned and specificity-tuned, were developed using ∼1,700 spectral variables per patient to prioritize either sensitivity or specificity. RESULTS: There were no statistically significant differences in patient characteristics between groups, as patients with negative results for endometriosis presented with other gynaecological disorders. The sensitivity-tuned algorithm achieved 93 % sensitivity and 57 % specificity, while the specificity-tuned version reached 93 % specificity but only 27 % sensitivity. Given an endometriosis prevalence of 30 % in symptomatic population, the sensitivity-tuned test reduced unnecessary MRI referrals by 42 %, prioritizing patients most likely to have endometriosis. The analysis time was 40 s per replicate, enabling same-day results. CONCLUSION: This proof-of-concept study demonstrates the clinical potential of a rapid, urine-based ML test to reduce diagnostic delays and imaging costs. Validation in larger, multi-center cohorts is underway to enhance robustness and generalizability.