Deep machine learning application to the detection of preclinical neurodegenerative diseases of aging

Mathew J Summers; Tamas Madl; Alessandro E Vercelli; Georg Aumayr; Doris M Bleier; Ludovico Ciferri

doi:10.4399/97888255088952

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Deep machine learning application to the detection of preclinical neurodegenerative diseases of aging

Journal article

Open access

Peer reviewed

Deep machine learning application to the detection of preclinical neurodegenerative diseases of aging

Mathew J Summers, Tamas Madl, Alessandro E Vercelli, Georg Aumayr, Doris M Bleier and Ludovico Ciferri

DigiCult: Scientific Journal on Digital Cultures, Vol.2(2), pp.9-24

2017

DOI: https://doi.org/10.4399/97888255088952

Appears in UniSC Diversity and Inclusion Research Collection

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Abstract

Neurosciences

Psychology

Clinical Sciences

artificial intelligence

deep learning

dementia

mild cognitive impairment

ageing

aging

neurodegenerative disease

diagnosis

preclinical

prodrome

UniSC Diversity Area - Disability and Inclusion

UniSC Diversity Area - Life Stages

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 agingrelated 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.

Details

Title: Deep machine learning application to the detection of preclinical neurodegenerative diseases of aging
Authors: Mathew J Summers (Author) - University of the Sunshine Coast - Faculty of Arts, Business and Law
Tamas Madl (Author) - Austrian Research Institute for Artifical Intelligence (OFAI), Austria
Alessandro E Vercelli (Author) - University of Turin, Italy
Georg Aumayr (Author) - Johanniter Austria Research and Innovation Centre, Austria
Doris M Bleier (Author) - Johanniter Austria Research and Innovation Centre, Austria
Ludovico Ciferri (Author) - International University of Japan, Japan
Publication details: DigiCult: Scientific Journal on Digital Cultures, Vol.2(2), pp.9-24
Publisher: Aracne editrice
Date published: 2017
DOI: 10.4399/97888255088952
ISSN: 2531-5994
Copyright note: Copyright rests with the authors. This work is released under a Creative Commons Attribution (IT) Licence, version 3.0. For details please see http://creativecommons.org/ licenses/by/3.0/it/
Organisation Unit: School of Health; School of Social Sciences - Legacy; University of the Sunshine Coast, Queensland; School of Health - Psychology; School of Health and Behavioural Sciences - Legacy
Language: English
Record Identifier: 99450676702621
Output Type: Journal article

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