Deep Machine Learning Application to the Detection of Preclinical Neurodegenerative Diseases of Aging

Abstract

Artificial intelligence (AI) deep learning protocols offer solutions to complex data processing and analysis. Increasingly these solutions are being applied in the healthcare field, most commonly in processing complex medical imaging data used for diagnosis. Current models apply AI to screening populations of patients for markers of disease and report detection accuracy rates exceeding those of human data screening. In this paper, we explore an alternate model for AI deployment, that of monitoring and analysing an individual’s level of function over time. In adopting this approach, we propose that AI may provide highly accurate and reliable detection of preclinical disease states associated with aging-related neurodegenerative diseases. One of the key challenges facing clinical detection of preclinical phases of diseases such as dementia is the high degree of inter-individual variability in aging-related changes to cognitive function. AI based monitoring of an individual over time offers the potential for the early detection of change in function for the individual, rather than relying on comparing the individual’s performance to population norms. We explore an approach to developing AI platforms for individual monitoring and preclinical disease detection and examine the potential benefits to the stakeholders in this technological development.

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