Raman spectroscopy as a non-invasive diagnostic technique for endometriosis

Uğur Parlatan, Medine Tuna Inanc, Bahar Yüksel, Bahar Yuksel Ozgor, Engin Oral, Ercan Bastu, Ercan Baştu, Mehmet Burcin Unlu, Mehmet Burçin Ünlü, Gunay Basar, Günay Başar
Scientific reports · 2019 · vol. 9(1) , pp. 19795 · doi:10.1038/s41598-019-56308-y · PMID:31875014 · PMC6930314 · W2996900882
article OA: gold CC0 ⤵ 9 in-corpus citations
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⚙ AI-generated summary by claude@2026-06, 2026-06-07 ⓘ

This study utilized Raman spectroscopy on blood serum samples to develop a non-invasive diagnostic method for endometriosis, achieving high sensitivity and specificity in classification models.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-07 ⓘ

This study evaluated whether Raman spectroscopy of blood serum could distinguish endometriosis patients from healthy controls using Raman spectra from 94 serum samples (49 patients, 45 controls) analyzed with PCA followed by classification using k-nearest neighbors (kNN) and support vector machines (SVM). After selecting the 790–1729 cm−1 spectral interval and using 8 PCs (covering 95% of total variance), kNN-weighted produced the best classification performance, with sensitivity and specificity of 80.5% and 89.7% on an internal split (80 training, 14 test), and 100%/100% when tested on unseen data. The paper notes that some higher-variance spectral regions (500–750 cm−1) were excluded and that the importance of specific biochemical bands for endometriosis is not yet clear. This paper is centrally about endometriosis — it tests Raman spectroscopy of blood serum as a non-invasive diagnostic approach for endometriosis.

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Abstract

Endometriosis is a condition in which the endometrium, the layer of tissue that usually covers the inside of the uterus, grows outside the uterus. One of its severe effects is sub-fertility. The exact reason for endometriosis is still unknown and under investigation. Tracking the symptoms is not sufficient for diagnosing the disease. A successful diagnosis can only be made using laparoscopy. During the disease, the amount of some molecules (i.e., proteins, antigens) changes in the blood. Raman spectroscopy provides information about biochemicals without using dyes or external labels. In this study, Raman spectroscopy is used as a non-invasive diagnostic method for endometriosis. The Raman spectra of 94 serum samples acquired from 49 patients and 45 healthy individuals were compared for this study. Principal Component Analysis (PCA), k- Nearest Neighbors (kNN), and Support Vector Machines (SVM) were used in the analysis. According to the results (using 80 measurements for training and 14 measurements for the test set), it was found that kNN-weighted gave the best classification model with sensitivity and specificity values of 80.5% and 89.7%, respectively. Testing the model with unseen data yielded a sensitivity value of 100% and a specificity value of 100%. To the best of our knowledge, this is the first study in which Raman spectroscopy was used in combination with PCA and classification algorithms as a non-invasive method applied on blood sera for the diagnosis of endometriosis.

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Condition tags

endometriosis

MeSH descriptors

Endometriosis Spectrum Analysis, Raman Adult Algorithms Endometriosis Endometriosis Female Humans Predictive Value of Tests Principal Component Analysis Reproducibility of Results Sensitivity and Specificity Support Vector Machine

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