Centre for Sensors, Instruments and Systems Development

Universitat Politècnica de Catalunya

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10/21/2021
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BIOPHOCH: Improving the diagnosis of visual, neurological and hematological diseases

Biomedical photonics is producing revolutionary scientific discoveries that improve the health of citizens. In fact, light-based instruments are currently used for non-invasive diagnostics and therapies. In parallel, efficient algorithms are needed to process the large amount of information collected by these instruments. Recent advances in machine learning have demonstrated its ability to classify and identify patterns in data, with reliability at the level of experienced clinicians, which can help improve diagnoses and treatments. Within this framework, the BIOPHOCH project aims to advance the use of photonic technologies in order to develop new tools and methods for image and data analysis that can significantly improve the diagnosis and treatment of diseases. In particular, the project will use eye-tracking technology, spectral and confocal microscopy, among others, together with data analysis and machine learning algorithms to investigate and face challenges in 3 medical areas:
  • The diagnosis of ocular disorders using new methods of refraction and evaluation of the eye fundus
  • The objective evaluation of cognitive state (Alzheimer's and persistent COVID) from the analysis of ocular movements
  • The diagnosis of haematological diseases through spectral confocal microscopy.
BIOPHOCH brings together a multidisciplinary team of physicists, optical, biomedical and software engineers, optometrists and physicians  67690t complementary expertise from the CD6 (UPC) and several hospitals: Institute of Ocular Microsurgery (IMO), Consorci Sanitari de Terrassa (CST) and Hospital Sant Joan de Déu (HSJD). It also has the support of companies in the medical sector. The project is based on the knowledge and experience acquired during the previous project financed by the Programa Estatal de I+D+i Orientada a los Retos de la Sociedad (DPI2017-89414-R).

Grant PID2020-112527RB-I00 funded by MCIN/AEI/ 10.13039/501100011033.
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CD6 Centre for Sensors, Instruments and Systems Development
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