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| Name | Class |
|---|---|
| Conselho Nacional de Desenvolvimento CientÃfico e Tecnológico | OTHER_GOV |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Pelvic Pain | Experimental | Patients referred to the gynecology outpatient clinic, with complain of pelvic pain. Positives and negatives for endometriosis will be outlined by an expert gynecologist according to the following criteria:
The intervention is the use of the ATR-FTIR Spectrometer in patient's urine samples to develop and validate a tool for detecting endometriosis. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| FTIR spectroscopy analysis | Diagnostic Test | ATR-FTIR Spectroscopy analysis combined with machine learning algorithms. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Spectroscopy 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. | 1 year |
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Inclusion Criteria:
Exclusion Criteria:
Biological woman self-identified as woman
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Valerio G Barauna, PhD | Contact | +5527996892407 | barauna2@gmail.com |
| Name | Affiliation | Role |
|---|---|---|
| Valerio G Barauna, PhD | Universidade Federal do EspÃrito Santo | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University Hospital Cassiano Antonio Moraes at Federal Univeristy Of EspÃrito Santo | Recruiting | Vitória | EspÃrito Santo | 29041-295 | Brazil |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 40915181 | Derived | Martins MS, Valente GB, Pedra YDN, Ribeiro TC, Boldrini NAT, Barcelos MRB, Martin FL, Rossi KKC, Barauna VG. A machine learning approach towards endometriosis screening using infrared spectra of urine. Clinics (Sao Paulo). 2025 Jan-Dec;80:100760. doi: 10.1016/j.clinsp.2025.100760. Epub 2025 Sep 6. |
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| ID | Term |
|---|---|
| D004715 | Endometriosis |
| D004194 | Disease |
| ID | Term |
|---|---|
| D005831 | Genital Diseases, Female |
| D052776 | Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
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| D000091662 | Genital Diseases |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |