AI-based autism identification from hyperspectral imaging detection of oxidative stress in pediatric red blood cells

BACKGROUND: Oxidative stress (OS) plays a key role in many pathologies, yet the non-invasive, label-free, and cost-effective detection remains a challenge. This study evaluates Hyperspectral Imaging (HSI) combined with AI to detect OS by identifying changes in red blood cell (RBC) membranes. METHODS: ) to induce stress without cell lysis. Membrane fatty acid composition (lipidome) is analysed via gas chromatography, while HSI in dark-field microscopy captures spectral signatures and their distributions in healthy and insulted RBC. The HSI methodology is then applied to RBC samples from 31 neurotypical (NT) children and 27 children with Autism Spectrum Disorder (ASD), a condition linked to OS. A deep learning algorithm is used to classify the clinical samples based on the identified OS signatures. RESULTS: model mirror those observed between the ASD and NT groups. The AI-assisted analysis successfully classifies the pediatric cohort, achieving 93.2% accuracy in identifying ASD subjects. CONCLUSIONS: HSI, guided by OS-specific modeling and integrated with AI, provides a robust, scalable method for membrane diagnostics. This approach offers a promising pathway for personalized medicine and the non-invasive monitoring of oxidative stress-related conditions.

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Publication Details

Journal
Communications Medicine
Published
2026-04-14
DOI
https://doi.org/10.1038/s43856-026-01581-y
Primary Topic
Autism Spectrum Disorder Research
Type
article
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article

AI-based autism identification from hyperspectral imaging detection of oxidative stress in pediatric red blood cells

Arianna Vignini, Gessica Batani, Alessandro Ghezzo, Carla Ferreri et al.
Communications Medicine
Autism Spectrum Disorder Research
article

AI-based autism identification from hyperspectral imaging detection of oxidative stress in pediatric red blood cells

Arianna Vignini, Gessica Batani, Alessandro Ghezzo, Carla Ferreri, Anna Sansone, Marida Angotti, Vincenzo Davide Catania, Ellas Spyratou, Antonia Parmeggiani, Chryssostomos Chatgilialoglu, Cristina Panisi, Marina Marini, Carmela Serpe, Roupen Vartian, Efstathios P. Efstathoupoulos, Enzo Grossi, Provvidenza M. Abruzzo, Mario Lima
article en

Abstract

BACKGROUND: Oxidative stress (OS) plays a key role in many pathologies, yet the non-invasive, label-free, and cost-effective detection remains a challenge. This study evaluates Hyperspectral Imaging (HSI) combined with AI to detect OS by identifying changes in red blood cell (RBC) membranes. METHODS: ) to induce stress without cell lysis. Membrane fatty acid composition (lipidome) is analysed via gas chromatography, while HSI in dark-field microscopy captures spectral signatures and their distributions in healthy and insulted RBC. The HSI methodology is then applied to RBC samples from 31 neurotypical (NT) children and 27 children with Autism Spectrum Disorder (ASD), a condition linked to OS. A deep learning algorithm is used to classify the clinical samples based on the identified OS signatures. RESULTS: model mirror those observed between the ASD and NT groups. The AI-assisted analysis successfully classifies the pediatric cohort, achieving 93.2% accuracy in identifying ASD subjects. CONCLUSIONS: HSI, guided by OS-specific modeling and integrated with AI, provides a robust, scalable method for membrane diagnostics. This approach offers a promising pathway for personalized medicine and the non-invasive monitoring of oxidative stress-related conditions.

Communications MedicineVol. 6(1)
Marche Polytechnic University (IT), National and Kapodistrian University of Athens (GR), Fondazione Roma (IT), Azienda USL di Bologna (IT), Institute of Organic Synthesis and Photoreactivity (IT), Istituto delle Scienze Neurologiche di Bologna (IT), Bologna Research Area (IT), IRCCS Eugenio Medea (IT), Istituti di Ricovero e Cura a Carattere Scientifico (IT), Adam Mickiewicz University in Poznań (PL), University of Bologna (IT)
Openalex Percentile: Top 26%
Autism Spectrum Disorder Research
DOIOpen Access PDFOpenAlex
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