Pinto, Mayara Moniz VieiraArisawa, Emilia Angela Lo SchiavoRaniero, Leandro JoséBhattacharjee, Tanmoy2025-08-262025-08-26https://repositorio.univap.br/handle/123456789/1041The diagnosis of Autism Spectrum Disorder (ASD) remains a challenge due to the lack of specific tests and biological markers. ASD is a neurodevelopmental disorder that affects in- dividuals throughout their lives, and its diagnosis allows access to treatments that improve their prognosis. Saliva analysis by Fourier Transform Infrared Spectroscopy (FTIR), which was not previ- ously reported, appears to be a promising diagnostic tool for ASD. This study acquired spectra from samples of 19 ASD and 19 control children. Spectral signatures suggest the dominance of protein secondary structures, β-pleated sheet and α-helix structures in ASD and control children, respectively. Support Vector Machine (SVM) gave the best diagnosis, with sensitivity, precision, and specificity being 92%, 94%, and 95%, respectively. Shapley values analysis to understand the impact of spectral features on the SVM classifier identified β-pleated and β-turn sheets as responsible for classification. Results indicate the potential of saliva-based FTIR for autism diagnosis, warranting a large-scale trial.PDFen-USArtigos de PeriódicosIEEE Photonics Journal10.1109/JPHOT.2025.3561020Autism spectrum disorderDiagnosisFTIRMachine learningPINTO, M. M. V. et al. Saliva FTIR Spectra and Machine Learning for Autism Spectrum Disorder Diagnosis-Preliminary Study. IEEE Photonics Journal, v. 17, n. 3, p. 1-4, 2025. Disponível em: 10.1109/JPHOT.2025.3561020.Universidade do Vale do Paraíba