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

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 dev

SUBMITTER: Martins MS 

PROVIDER: S-EPMC12450736 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature