Potential use of saliva infrared spectra and machine learning for a minimally invasive screening test for congenital syphilis in infants.

Congenital syphilis is a global public health issue, and its diagnostic complexity poses a challenge to early treatment. Fourier Transform Infrared Spectroscopy (FTIR) is a promising technological tool that facilitates the detection and diagnosis of various diseases by providing information on the biochemical composition of biofluids, including saliva. However, the potential use of FTIR in congenital syphilis has not yet been studied. This study aimed to explore the development of a method for the diagnosis of congenital syphilis using saliva FTIR spectra and machine learning algorithms in infants aged 0 to 12 months. First, the potential of FTIR for analyzing infant saliva was evaluated. Spectral analysis revealed subtle differences in vibrational modes between the test and control groups. Complementary analyses, such as Principal Component Analysis (PCA) and Leave-One-Out Cross-Validation (LOOCV), were used to assess the variance among samples, which enabled efficient discrimination and highlighted the relevance of the observed variance between groups. When applying Quadratic Standard Normal Variate preprocessing with LOOCV, the model achieved 90% accuracy, 100% sensitivity, and 80% specificity. Therefore, the method demonstrated potential as a screening test for congenital syphilis. The study's limitations include a reduced sample size and the reliance on the data upsampling approach.