Machine Learning-Driven Spectroscopic Analysis for Early Detection of Autoimmune Diseases

16 Sept 2025, 16:45
5m
Contributed Poster Presentation Physics Research Poster Room

Speaker

Sarra Ben Brik (Laboratory of Atomic and Molecular Spectroscopy & Applications, Faculty of Sciences, University of Tunis El Manar)

Description

This study focuses on the development of innovative screening techniques for autoimmune diseases, particularly Systemic Lupus Erythematosus (SLE), aiming to overcome the limitations of invasive and costly analyses. By leveraging non-invasive spectroscopic methods, along with the analysis of easily acquired biological tissues like nails, hair, and skin, the research aims to enable rapid, real-time, and cost-effective disease detection on-site. The investigation involves comparing the results of tissue analyses with traditional blood analyses, encompassing patients at different disease stages. The objective is to establish criteria for disease prevention and progression through cluster analysis, drawing from extensive expertise in spectroscopic techniques and previous studies in related pathologies. Additionally, the study explores the potential of machine learning to automate screening processes, anticipating significant contributions to diagnosis prediction and classification of autoimmune diseases.

Abstract Category Optics & Photonics

Author

Sarra Ben Brik (Laboratory of Atomic and Molecular Spectroscopy & Applications, Faculty of Sciences, University of Tunis El Manar)

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