PredictAD

Dementia causes long and oppressive suffering to patients and their relatives and imposes enormous costs on society. About 25 million people suffered from dementia in 2000. As a 4-fold increase of this number is expected by 2050, dementia is one main health issue of the next decades.

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.

For further information, please visit:
http://www.predictad.eu

Project co-ordinator:
Valtion teknillinen tutkimuskeskus VTT

Partners:

  • Nexstim Oy
  • Imperial College of Science, Technology and Medicine
  • Rigshospitalet
  • 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)


Related news article:

Most Popular Now

Philips Launches HealthSuite System of E…

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, today announced the HealthSuite System of Engagement, an integrated, modular set of standards-based capabilities that support the development...

AI may Help Spot Newborns at Risk for Mo…

An artificial intelligence (AI) device that has been fast-tracked for approval by the Food and Drug Administration may help identify newborns at risk for aggressive posterior retinopathy of prematurity (AP-ROP)...

International Scientific Symposium DigiH…

13 November 2020, Pfarrkirchen, Germany DigiHealthDay @DIT-ECRI is going to be a daylong action-packed event targeting primarily academia - from established researchers, to young scientists and students. Following the theme "How...

Siemens Healthineers Introduces Teamplay…

Siemens Healthineers announces market introduction of the teamplay digital health platform. With the teamplay digital health platform Siemens Healthineers paves the way for healthcare providers' digital transformation - facilitating easy...

Digital Heart Model will Help Predict Fu…

In recent times, researchers have increasing found that the power of computers and artificial intelligence is enabling more accurate diagnosis of a patient's current heart health and can provide an...

Fighting Hand Tremors: First comes AI, t…

Robots hold promise for a large number of people with neurological movement disorders severely affecting the quality of their lives. Now researchers have tapped artificial intelligence techniques to build an...

Oxford University Provide Evidence for C…

A team of medical research and bioethics experts at Oxford University are supporting several European governments to explore the feasibility of a coronavirus mobile app for instant contact tracing. If...

Portable AI Device Turns Coughing Sounds…

University of Massachusetts Amherst researchers have invented a portable surveillance device powered by machine learning - called FluSense - which can detect coughing and crowd size in real time, then...

Buddy Healthcare Launches COVID-19 Remot…

Buddy Healthcare wants to help hospitals and healthcare professionals in the battle against the COVID-19. BuddyCare virtual care platform can be used for not only symptom tracking, remote monitoring and...

Preventicus Appoints Ljubisav Matejevic …

Preventicus is proud to announce that the company continues to grow by bringing its unique, comprehensive care management programs for prevention of strokes and cardiovascular events to new international markets...

Philips Ramps Up Production of Critical …

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, announced that it is increasing the production of certain critical care products and solutions to help diagnose and...