SOURCE-LINKED INTELLIGENCE

Endoscopic tissue time machine

CORDIS · observation · Publication date unknown

gration of clinically relevant multi-modal endoscopic data, bridging different scales and modalities while putting endoscopic data in a clinical and biological context. We will develop an explainable deep learning model (an endoscopic time machine) to predict transition points in the progression from normal tissue to dysplasia and ultimately to cancer in an animal model. To address the unmet clinical need, we will conduct a longitudinal human observational study utilizing the endoscopic tissue time machine for real-time in vivo prediction of progression risk from dysplasia to cancer. This project will integrate scientific and technological advances to offer new insights into the microenvironment of H&N carcinogenesis, enhance understanding of clinical diversity, and identify novel transition biomarkers for improved prognostics and timely interventions. Multi-modal imaging, fibre-optics, endoscopy, Raman spectroscopy, Optical Coherence Tomography, head and neck cancer, deep learning

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recordType

award

status

SIGNED

region

EU

value

2273033

unit

EUR