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Background <p>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.</p> Methods <p>An OS model for the HSI procedure is established by treating EDTA-anticoagulated whole blood with 1.5% hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) 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.</p> Results <p>Here, we show that significant spectral distribution differences are present in OS-exposed RBCs, which correlate with membrane lipidome remodelling. Notably, the OS-induced spectral differences in the H<sub>2</sub>O<sub>2</sub> 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.</p> Conclusions <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月14日

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

摘要

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

An OS model for the HSI procedure is established by treating EDTA-anticoagulated whole blood with 1.5% hydrogen peroxide (H2O2) 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

Here, we show that significant spectral distribution differences are present in OS-exposed RBCs, which correlate with membrane lipidome remodelling. Notably, the OS-induced spectral differences in the H2O2 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.