BE-LIGHT Improving BiomEdical diagnosis through LIGHT-based technologies and machine learning

Photonics is crucial for maintaining and increasing the competitiveness of EU industry, and light-based instruments are fundamental for non-invasive diagnostics and treatment of diseases, delivering huge economic benefits for national health systems. Artificial Intelligence (AI) and Machine Learning (ML) have demonstrated their capability to classify and identify patterns in data, achieving reliability levels that are comparable to those of experienced physicians. To fully develop the potentials of these technologies, BE-LIGHT will provide top level training to 11 Researchers in photonic technologies (multispectral imaging, eye-tracking, optical coherence tomography, multispectral optoacoustic tomography, super-resolution microscopy and optogenetics) that will be complemented with AI/ML, computational imaging and modelling. A close-to-the-clinic approach will allow them to advance the field of biomedicine, with the ultimate goal of improving the understanding, diagnosis and treatment of diseases in 3 main research areas: visual health, cardiovascular health, and analysis of cells and tissues (related to Parkinson’s and rare diseases) using super-resolution microscopy.BE-LIGHT brings together an interdisciplinary consortium of 7 top academic institutions, 3 internationally recognized hospitals, and an ecosystem of 7 private companies with complementary know-how in photonics, microscopy, AI/ML, medical instruments and clinical research, to promote inter-sectoral synergies. BE-LIGHT’s multi-skill training program will provide the researchers with the complete set of skills that are nowadays key for their success in both academic and non-academic sectors. They will gain a broad understanding of how state-of-the-art light-based technologies work, a solid knowledge of ML and data processing, and will obtain experience in clinical and commercialization processes. BE-LIGHT will open for them a wide range of high-quality job opportunities.