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Artificial Intelligence Integrated Scattering Type Near Field Optical Microscopy for Early and Transformative Cancer Detection

Objectif

AI-SCAN aims to revolutionize the field of cancer diagnostics by merging the analytical power of artificial intelligence (AI) with the ultra-high spatial resolution of scattering-type near-field optical microscopy (SNOM) for cancer detection. Cancer cells undergo biochemical and morphological alterations at the nanoscale, such as changes in DNA, protein content, lipid distribution, refractive index (RI), and membrane architecture. Detecting and interpreting these subtle variations before they observable at the tissue or organ level remains one of the greatest challenges in early diagnosis. AI-SCAN directly addresses this challenge by developing intelligent computational models capable of decoding complex nanoscale optical signals and identifying disease-specific biomarkers with unprecedented sensitivity and accuracy. The project integrates three complementary components. First, physics-based three-dimensional (3D) simulation models will be established to reproduce the interaction between light, SNOM probe, and biological samples. Second, advanced SNOM imaging will be employed to acquire real time high-quality spectroscopic and topographic data from healthy and cancerous cells. Third, cutting-edge AI algorithms like BRANN will be utilized to extract diagnostically relevant features from multimodal SNOM datasets enabling robust classification of healthy versus malignant states. Through this multidisciplinary approach, AI-SCAN seeks to establish a new paradigm for nanoscale cancer detection. The combination of experimental measurements, computational simulations, and AI analysis will allow systematic identification of optical biomarkers that are otherwise hidden within noisy and high-dimensional datasets. Ultimately, AI-SCAN has the potential to pave the way for earlier and more accurate detection of aggressive cancers, contributing to the advancement of biomedical imaging and strengthening Europe’s leadership in AI-driven healthcare innovation.

Champ scientifique (EuroSciVoc)

Mots‑clés

Programme(s)

Thème(s)

Régime de financement

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Appel à propositions

(s’ouvre dans une nouvelle fenêtre) HORIZON-MSCA-2025-PF

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Coordinateur

VILNIAUS UNIVERSITETAS
Contribution nette de l'UE
€ 196 976,16
Adresse
UNIVERSITETO G. 3
01513 Vilnius
Lituanie
Région
Lietuva Sostinės regionas Vilniaus apskritis
Type d’activité
Higher or Secondary Education Establishments
Liens
Coût total
Aucune donnée

Partenaires (1)