Alzheimer's disease (AD) covers 60-70% of all dementia cases. No cure for AD exists, and effective and reliable early diagnostic techniques are lacking. Early diagnosis and progress monitoring of AD is a central part of treatment once future drugs and prevention strategies become available. There is a strong indication that different biomarkers provide a reliable and early indication of AD prior to its major clinical signs. However, optimal early diagnosis requires information from a combination of different biomarkers to be used in a clinically useful way.
The objective of PredictAD is 1) to find the best combination of biomarkers for AD diagnostics from heterogeneous data (imaging, electrophysiology, molecular level, clinical tests, demographics) and 2) to develop clinically useful tools integrating the optimal biomarker results. Comprehensive biomarker discovery techniques and rigorous statistical models will be developed using the consortium's large databases. The accuracy and usability of models and tool will be clinically evaluated. The cost-effectiveness of heterogeneous data in AD diagnostic procedures will be studied.
By reaching its objectives, PredictAD provides an efficient and reliable solution for early AD diagnosis in clinical practice. The impacts on patients, their relatives and society are reduced suffering and costs. As we are living in the dawn of an era of new drugs and prevention strategies combined with increasing AD prevalence, now is the time to exploit the vast potential of information hiding in heterogeneous patient databases. PredictAD combines the best forces in Europe to solve the AD diagnostics problem, and hence strengthens EU leadership on the market.
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Valtion teknillinen tutkimuskeskus VTT
- Nexstim Oy
- Imperial College of Science, Technology and Medicine
- Kuopion yliopisto
- Università degli Studi di Milano
- GE Healthcare Ltd.
- Uppsala universitet
Timetable: from 06/2008 to 05/2011
Total cost: 3.981.565
EC funding: 2.891.526
Programme Acronym: FP7-ICT
Subprogramme Area: Virtual physiological human
Contract type: Collaborative project (generic)
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- A Rapid Image Analysis Method to Diagnose Alzheimer's Disease
- New FP7 eHealth Project - PredictAD
- European Research Project to Explore Alzheimer's Disease Diagnosis