Descrizione del progetto
Microscopia Raman coerente nella diagnostica del cancro
Il progetto CHARM, finanziato dall’UE, intende portare l’istopatologia digitale del cancro a un livello superiore, introducendo una tecnologia in grado di misurare la composizione molecolare di un tessuto e di caratterizzare i tumori senza marcature. CHARM svilupperà uno strumento medico basato su un microscopio Raman a diffusione coerente a banda larga dotato di un modulo IA integrato basato su apprendimento profondo, statistica e apprendimento automatico. L’integrazione con l’intelligenza artificiale offrirà un sistema di supporto alle decisioni cliniche rapido e affidabile per la diagnosi del cancro e la terapia personalizzata. Il sistema chemiometrico di patologia sviluppato sarà in grado di analizzare tessuti non colorati, identificando il tumore con un’accuratezza superiore al 98 % e prevedendo la diagnosi del tumore con un’accuratezza superiore al 90 %.
Obiettivo
The CHARM project aims to radically transform the cancer diagnosing process and bring the emerging field of digital histopathology to the next level, introducing a novel technology for tissue analysis, capable to measure the molecular composition of the patient tissue samples and to recognize and classify the tumor in a completely label/stain-free way. The instrument, integrated with artificial intelligence (AI), will offer to histopathologists a reliable, fast and low-cost Clinical Decision Support System (CDSS) for cancer diagnosis and personalized cancer therapy. We will develop a Class C, (IVDR, In-Vitro Diagnostic Regulation) medical device consisting of a turnkey low-cost broadband Coherent Raman Scattering (CRS) microscope (enabled by our patented graphene-based fiber laser technology), named the Chemometric Pathology System (CPS), integrating an AI module based on deep learning, statistics and machine learning. The CPS will be capable of automatically analyzing unstained tissues, providing fast and accurate tumour identification (differentiating normal vs neoplastic tissues) with accuracy >98% and final tumour diagnosis prediction (differentiating and grading histologic subtypes) with accuracy >90%, thus offering to the histopathologist a decision tree compatible with existing clinical protocols but with biomolecular-based objectivity and reduced time to result (TRL6). We will develop a robust business case for the application and ensure the project continuation to higher TRLs and the final market entrance. This proposal builds on the results of the ERC POC project GSYNCOR.
Campo scientifico
- natural sciencesphysical sciencesopticsmicroscopy
- medical and health sciencesclinical medicineoncology
- medical and health scienceshealth sciencespersonalized medicine
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencesphysical sciencesopticslaser physics
Parole chiave
Programma(i)
- HORIZON.3.1 - The European Innovation Council (EIC) Main Programme
Argomento(i)
Invito a presentare proposte
HORIZON-EIC-2021-TRANSITION-CHALLENGES-01
Vedi altri progetti per questo bandoMeccanismo di finanziamento
HORIZON-EIC - HORIZON EIC GrantsCoordinatore
37126 Verona
Italia
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