Early Detection in Alzheimer's Disease
  • Author : Dennis Chan
  • Release Date : 15 September 2021
  • Publisher : Academic Press
  • Genre : Medical
  • Pages : 300
  • ISBN 13 : 0128222409
  • Total Download : 930
  • File Size : 44,5 Mb

Early Detection in Alzheimer's Disease PDF Summary

Early Detection of Alzheimer's Disease aims to introduce to a wide audience the high global priority problem of detecting AD prior to dementia onset. According to the Alzheimer's Association, 5.8 million Americans are living with Alzheimer's and care costs will cost the nation approximately $290 billion (2019 Alzheimer's Disease Facts and Figures). With the failure of recent AD drug trials, many hypothesize that by the time symptoms appear, it is too late to be treated. Early detection can offer benefits such as more choice of medications, ability to participate in clinical trials, more time for family and for care planning. This book outlines potential solutions to the above problem using opportunities arising from the technology revolution, advances in neuroscience, and molecular biology. Most importantly, it discusses a paradigm shift from a reactive to a proactive diagnostic approach, aiming to detect disease before occurrence of symptoms. Topics covered include the use of sensing technologies (eg smartphones, smartwatches, Internet of Things) to detect early disease-related changes , the application of data science (machine learning/AI) to extract otherwise invisible disease features from these datasets and the potential to personalize diagnosis based on tracking changes in individual behaviours. Advances in blood-based biomarkers, brain imaging, and the potential for early diagnosis to aid interventions (lifestyle, dietary, pharmacological) to delay future development of dementia are also discussed. Outlines the importance of early diagnosis of Alzheimer's Disease Helps readers understand the limitations of current clinical approaches and the need for a paradigmatic shift in diagnostic practice Discusses the potential role of technology in clinical practice using machine learning and artificial intelligence and the potential to personize diagnosis and treatment