Artificial intelligence and model based multimodal approach for the detection, classification and mechanistic understanding of preclinical Alzheimer’s disease
Alzheimer’s disease (AD) is a growing public health challenge with no available cure. A major obstacle to effective treatment is the lack of understanding of complex inter-relations between the patients’ clinical manifestation and disease-specific mechanism(s) at the molecular and neuronal levels. The extremely long prodromal stage (preclinical AD), makes this understanding, and therefore early diagnosis, difficult. This has special importance as very recent evidence suggests that newly developed biological treatment modalities are effective only when introduced very early in the disease course. The research aims to address this challenge by developing a data-science based approach for the early identification and diagnosis of AD.
Developing new approach for the detection, classification and mechanistic understanding of preclinical Alzheimer’s disease, that may be also utilized in other disorders which are characterized by a long preclinical stage.
What we did