Machine learning for the life-time risk prediction of Alzheimer’s disease: a systematic review

Abstract

Alzheimer’s disease is a neurodegenerative disorder and the most common form of dementia. Early diagnosis may assist interventions to delay onset and reduce the progression rate of the disease. We systematically reviewed the use of machine learning algorithms for predicting Alzheimer’s disease using single nucleotide polymorphisms and instances where these were combined with other types of data. We evaluated the ability of machine learning models to distinguish between controls and cases, while also assessing their implementation and potential biases. Articles published between December 2009—June 2020 were collected using Scopus, PubMed and Google Scholar. These were systematically screened for inclusion leading to a final set of 12 publications. Eighty-five percent of the included studies used the Alzheimer's Disease Neuroimaging Initiative dataset. In studies which reported area under the curve, discrimination varied (0.49–0.97). However, more than half of the included manuscripts used other forms of measurement such as accuracy, sensitivity and specificity. Model calibration statistics were also found to be reported inconsistently across all studies. The most frequent limitation in the assessed studies was sample size, with the total number of participants often numbering less than a thousand, whilst the number of predictors usually ran into the many thousands. In addition, key steps in model implementation and validation were often not performed or unreported, making it difficult to assess the capability of machine learning models.

Harnessing the potential of machine learning and artificial intelligence for dementia research

“…These scores have substantial potential utility in predicting lifetime risk of dementia. However, major challenges remain including how these scores can be used reliably at the individual level, their application across ethnicities, and whether their applicability varies at different stages of the lifecourse [ 15 ], plus the potential use of genetics and wide-ranging -omics generally in predictive modelling and machine learning [ 16 , 17 ].…”

A partnership between occupational therapy and dentistry

“…Sensory sensitivities associated with autism can also pose challenges for the dental team, impacting the provision of dental care. 1 As a result, autistic children and adolescents are more likely than their neurotypical peers to receive treatment under general anaesthesia. 1 Our team is collaborating with Stacey Venner, occupational therapist (OT) and experienced dental nurse who encountered first-hand the barriers to dental engagement for those from the autistic community and identified a need for multi-professional working that incorporates an occupational therapy perspective.…”

“…1 As a result, autistic children and adolescents are more likely than their neurotypical peers to receive treatment under general anaesthesia. 1 Our team is collaborating with Stacey Venner, occupational therapist (OT) and experienced dental nurse who encountered first-hand the barriers to dental engagement for those from the autistic community and identified a need for multi-professional working that incorporates an occupational therapy perspective. Occupational therapy could be an ideal profession to assess and, where required, to provide a care plan for specific care needs of patients prior to dental visits.…”

Machine Learning Approaches and Applications in Genome Wide Association Study for Alzheimer’s Disease: A Systematic Review

“…A review of illness prediction based on singlenucleotide polymorphisms has been undertaken by Ho et al [12]. A recent systematic review of ML algorithms in SNP data of Alzheimer's disease is shown by Rowe et al [13]. However, the main limitation of the research paper provided by Rowe et al [13] is the fact that they have utilized machine learning as a keyword for their search and presentation instead of investigating specific ML techniques.…”

“…A recent systematic review of ML algorithms in SNP data of Alzheimer's disease is shown by Rowe et al [13]. However, the main limitation of the research paper provided by Rowe et al [13] is the fact that they have utilized machine learning as a keyword for their search and presentation instead of investigating specific ML techniques. Hence, their review paper does not provide sufficient details on the contribution of various research in relation to the use of ML for the analysis of SNP data of AZ.…”