ATR-FTIR Spectroscopy of Saliva and Machine Learning as a Screening Test for Sjogren Disease.

ABSTRACT: Sjögren's disease is often an underdiagnosed autoimmune condition that primarily affects the exocrine glands, resulting in symptoms such as dry eyes and dry mouth. Diagnostic challenges stem from nonspecific symptoms, the absence of definitive biomarkers, and the invasiveness and cost associated with current clinical tests. This study investigates the application of attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR), in conjunction with a neural network and Monte Carlo dropout-based uncertainty estimation to distinguish patients with Sjögren's disease from healthy individuals. Uncertainty estimates were used to identify ambiguous spectra and refine training data, improving predictive performance and enabling confidence-aware interpretation. To minimize bias rel

SUBMITTER: Contreras J 

PROVIDER: S-EPMC12676517 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature