A machine learning approach towards endometriosis screening using infrared spectra of urine

Matthews Silva Martins, Gabriela Barros Valente, Gianluca Valente, Yasmin do Nascimento Pedra, Thaís Campos Ribeiro, T. Ribeiro, Neide Aparecida Tosato Boldrini, Mara Rejane Barroso Barcelos, Francis L Martin, Karin Kneipp Costa Rossi, Karin Rossi, Valerio Garrone Barauna, Valério Garrone Baraúna
Clinics (Sao Paulo, Brazil) · 2025 · vol. 80 , pp. 100760 · doi:10.1016/j.clinsp.2025.100760 · PMID:40915181 · PMC12450736 · W4414034502
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⚙ AI-generated summary by claude@2026-06, 2026-06-09 ⓘ

This study developed a rapid, urine-based machine learning test using ATR-FTIR spectroscopy that demonstrated potential for screening endometriosis, reducing unnecessary MRI referrals by 42% with a sensitivity-tuned algorithm.

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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.

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Condition tags

endometriosis

MeSH descriptors

Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Machine Learning Machine Learning Machine Learning Machine Learning Machine Learning Machine Learning

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

References (36)

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