Mid-infrared spectroscopy as a real-time diagnostic tool for chronic endometritis: A preliminary study

Asaf Bilgory; Sergio Haimovich; Liron Vardi David; Dov Malonek; Ben Zion Dekel; Lea Shechtman; Gabriel M. Groisman; Einat Shalom‐Paz

doi:10.1016/j.saa.2024.125670

Mid-infrared spectroscopy as a real-time diagnostic tool for chronic endometritis: A preliminary study

Asaf Bilgory, Sergio Haimovich, Liron Vardi David, Dov Malonek, Ben Zion Dekel, Lea Shechtman, Gabriel M. Groisman, Einat Shalom‐Paz

In: Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy · 2024 · vol. 330 , pp. 125670 · doi:10.1016/j.saa.2024.125670 · PMID:39765047 · W4405846962

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This preliminary study investigated the utility of mid-infrared spectroscopy as a real-time diagnostic method for chronic endometritis.

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Abstract

Research questionCan attenuated total reflection-Fourier transform infrared spectroscopy combined with machine learning techniques be used to develop a real-time diagnostic modality for chronic endometritis by analysing endometrial biopsies obtained during hysteroscopy?DesignWomen undergoing hysteroscopy for infertility assessment were enrolled in this prospective study from January 2020 to March 2021. Endometrial biopsies were evaluated using a spectrophotometer, and subsequently via histopathology, including immunohistochemical staining for the multiple myeloma oncogene-1 (MUM-1). Spectroscopy analyses of the positive and the negative chronic endometritis groups were compared across various cut-offs of MUM-1 positive cells per 10 high-power fields (HPF). Machine learning techniques were used to build discrimination models with Matlab and Unscrambler software packages.ResultsFifty-four women were recruited. Four of the 54 measured spectra were discarded due to high measurement noise. Fifty biopsies were finally assessed, and a discriminant model was developed using the Principal Component Analysis and Linear Discriminant Analysis techniques (machine learning). The model was evaluated for accuracy using different MUM-1 cut-offs. Distinct spectral disparities (p 7 MUM-1 cells/10 HPF, the model differentiated positive and negative chronic endometritis with 84 % accuracy, 88.8 % sensitivity and 82.9 % specificity.ConclusionsOur findings in this preliminary study support the potential availability of a bedside diagnostic tool based on a model developed using spectroscopy coupled with machine learning. This allowed us to diagnose chronic endometritis in real-time with 84% accuracy, facilitating immediate initiation of appropriate treatment.