European research project tp explore Alzheimer's disease diagnosis
VTT Technical Research Centre of Finland, GE Healthcare, a division of the General Electric Company and a Consortium of leading research institutions across Europe announced the PredictAD project, a research collaboration aimed at developing new diagnostic procedures for patients with Alzheimer's disease. Today there is no single test or biomarker that can predict whether a particular person will develop Alzheimer´s disease and a definitive diagnosis is only possible after death - with postmortem analysis.
PredictAD is an EU funded research project which will study imaging biomarkers (MRI, PET FDG and PET PIB), electrical brain activity measurement and blood based markers (proteomics and metabolomics) and develop methods for how to combine data from different biomarkers. Combining this multisource information may enable earlier diagnosis of Alzheimer's disease, but may also provide crucial information used for differentiating between various forms of dementia and for assessing disease severity. It may also allow for improved detection of disease progression and treatment efficacy monitoring.
"The aim of PredictAD project is to develop an objective indicator to diagnose Alzheimer's disease at the earliest stage possible. This may be possible by combining data from various data sources of patient monitoring, such as neuropsychological tests, medical imaging, electrical brain activity measurements, and analyzing protein and metabolomics levels of blood samples. Early diagnostics may play an important role in effective medical treatment of Alzheimer's disease, especially in the future, as the next generation of more effective therapies become available for all patients," says the Scientific Coordinator of the project, Dr. Jyrki Lötjönen from VTT.
At a later stage during this three-year project, a selected biomarker set will be used to develop an efficient and reliable software solution that can be used by a physician to assess the risk, to diagnose and to monitor the progress of Alzheimer's disease in real clinical conditions using various patient data. The accuracy, usability and cost-effectiveness of the models and software will be clinically evaluated.
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