The goal of this study is to explore the use of mid-infrared spectroscopy (ATR-FTIR) as a detection tool for endometriosis in urine.
Endometriosis is a chronic gynecological disease that is considered debilitating and multifactorial. Its diagnosis is invasive and can be prolonged due to non-specific symptoms and erroneous or late investigations, which can lead to delays and impair the provision of adequate treatment. ATR-FTIR Spectroscopy is a non-invasive technique with the capability to identify the chemical composition and molecular changes of samples through its interaction with mid-infrared radiation. The aim of this work is to develop a rapid test for the detection of endometriosis in urine samples using spectroscopy and machine learning algorithms.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Enrollment
600
ATR-FTIR Spectroscopy analysis combined with machine learning algorithms.
University Hospital Cassiano Antonio Moraes at Federal Univeristy Of Espírito Santo
Vitória, Espírito Santo, Brazil
RECRUITINGSpectroscopy Reliability (diagnostic metrics)
The primary outcome is the evaluation of specificity, sensitivity and accuracy of the diagnostic. Acceptable diagnostic metrics must be comparable to MRI, which will demonstrate if spectroscopy can discriminate between negative and positive endometriosis patients.
Time frame: 1 year
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