{"id":18377,"date":"2024-02-07T19:04:47","date_gmt":"2024-02-07T19:04:47","guid":{"rendered":"https:\/\/interhospi.com\/?p=18377"},"modified":"2024-02-07T19:05:27","modified_gmt":"2024-02-07T19:05:27","slug":"be-light-project-to-develop-new-photonics-and-ai-tools-for-diagnosis-of-several-diseases","status":"publish","type":"post","link":"https:\/\/interhospi.com\/be-light-project-to-develop-new-photonics-and-ai-tools-for-diagnosis-of-several-diseases\/","title":{"rendered":"BE-LIGHT project to develop new photonics and AI tools for diagnosis of several diseases"},"content":{"rendered":"
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BE-LIGHT project to develop new photonics and AI tools for diagnosis of several diseases<\/h1>\/ in Featured Articles<\/a> <\/span><\/span><\/header>\n<\/div><\/section>
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A new European research project called BE-LIGHT has received \u20ac2.5 million in funding from the EU\u2019s Horizon Europe programme to develop new photonics and AI tools for the early detection and treatment of ocular, cardiovascular and neurodegenerative diseases. The project is coordinated by the Centre for Sensors, Instruments and Systems Development (CD6) at Universitat Polit\u00e8cnica de Catalunya, BarcelonaTech (UPC) in collaboration with seven academic institutions, three hospitals and seven companies from Germany, France, Poland, Switzerland and Spain.<\/h3>\n

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The consortium is led by Meritxell Vilaseca, a researcher at CD6 and a professor at the Terrassa School of Optics and Optometry (FOOT) and the Barcelona School of Telecommunications Engineering (ETSETB), and Cristina Masoller, a researcher at the Nonlinear Dynamics, Nonlinear Optics and Lasers (DONLL) research group and a professor at the Terrassa School of Industrial, Aerospace and Audiovisual Engineering (ESEIAAT).<\/p>\n<\/div><\/section>
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Researchers Cristina Masoller and Meritxell Vilaseca are co-ordinating the BE-LIGHT project<\/em><\/p>\n<\/div><\/section>
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Improving the diagnosis of retinal diseases<\/h4>\n

The BE-LIGHT project is a new step towards understanding and diagnosing retinal diseases, as explained by project coordinator Vilaseca: \u201cThrough the use of neural networks and optogenetic techniques, the new tools will better understand the functioning of the retina and the exchange of information between neurons, and thus, based on quantitative models, detect diseases in the retina.\u201d<\/p>\n

Vilaseca notes that \u201cthe combination of various photonic technologies, such as multispectral imaging and optical coherence tomography, complemented by AI algorithms, will allow for the precise analysis of ocular structures (cornea, vitreous, fundus) to detect visual diseases and oculomotor disorders early\u201d.<\/p>\n

In addition, according to Vilaseca, \u201cthe AI evaluation of the patterns of eye movements, strongly controlled by different brain regions, can also offer new diagnostic tools for neurological diseases, such as Alzheimer\u2019s or persistent Covid\u201d.<\/p>\n

Masoller says that \u201cthe project will also enable the development of new instruments and clinical methods capable of, for example, obtaining images of blood vessels or ocular structures of the patient through optical and optoacoustic tomography or thermal imaging\u201d.<\/p>\n

\u201cThis technology will help to non-invasively detect arteriosclerosis plaques early, as an example,\u201d she says.<\/p>\n

\u201cNew machine-learning tools will be developed for light-based arrhythmia treatment and control, which could replace current techniques based on electrical impulses,\u201d Masoller says. \u201cThe application of AI in combination with super-resolution microscopy techniques will make it possible to obtain images of biological structures less than one nanometre, such as proteins involved in Parkinson\u2019s disease and other rare diseases, improving their diagnosis.\u201d<\/p>\n

Additionally, as part of the BE-LIGHT project, 11 researchers will be offered a training programme in photonics, AI, machine learning, computer imaging and modelling.<\/p>\n<\/div><\/section>
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The international team working on the BE-LIGHT project<\/em><\/p>\n<\/div><\/section>
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