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  • Review Article
  • Published:

Applications of machine learning to diagnosis and treatment of neurodegenerative diseases

Nature Reviews Neurology volume 16, pages 440–456 (2020)Cite this article

Subjects

Abstract

Globally, there is a huge unmet need for effective treatments for neurodegenerative diseases. The complexity of the molecular mechanisms underlying neuronal degeneration and the heterogeneity of the patient population present massive challenges to the development of early diagnostic tools and effective treatments for these diseases. Machine learning, a subfield of artificial intelligence, is enabling scientists, clinicians and patients to address some of these challenges. In this Review, we discuss how machine learning can aid early diagnosis and interpretation of medical images as well as the discovery and development of new therapies. A unifying theme of the different applications of machine learning is the integration of multiple high-dimensional sources of data, which all provide a different view on disease, and the automated derivation of actionable insights.

Key points

  • Machine learning and natural language processing are forms of artificial intelligence that enable robust interrogation of multiple datasets to identify previously undiscovered patterns and relationships in the data.

  • Machine learning approaches have been applied to the study of neurodegenerative diseases and show promise in the areas of early diagnosis, prognosis and development of new therapies.

  • A substantial number of machine learning algorithms exist, and choosing the correct algorithm to apply to different types of data is crucial to obtain reliable results.

  • Neuroimaging was the first area of neurology to benefit from the application of machine learning approaches to improve diagnosis; more recently, application of machine learning methods to motor function and language feature analysis has shown promise in decreasing the time taken to perform clinical assessments.

  • The application of machine learning to longitudinal patient data collection and electronic health records has the potential to inform prognosis prediction and patient stratification.

  • Large collections of curated datasets and robust assessment of machine learning methods will be needed to achieve full integration of machine learning into diagnostic and prognostic neurology practice and the design of future therapeutics.

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Fig. 1: Categories of machine learning.
Fig. 2: Determining the best machine learning model for a given problem.
Fig. 3: Key developments and novel applications of computational and machine learning techniques to neuroimaging since the 1990s.
Fig. 4: Application of machine learning approaches to development and implementation of treatments for neurodegeneration.

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Authors and Affiliations

  1. Sheffield Institute of Translational Neuroscience, University of Sheffield, Sheffield, UK

    Monika A. Myszczynska, Richard Mead, Guillaume M. Hautbergue & Laura Ferraiuolo

  2. BenevolentAI, London, UK

    Poojitha N. Ojamies, Daniel Neil, Amir Saffari & Joanna D. Holbrook

  3. BenevolentAI, Brooklyn, NY, USA

    Alix M. B. Lacoste

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  1. Monika A. Myszczynska
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Contributions

L.F. researched data for the article, made a substantial contribution to the discussion of article content, wrote the article, and reviewed and edited the manuscript before submission. M.A.M. researched data for the article, made a substantial contribution to the discussion of article content and wrote the article. P.N.O. and J.D.H. made a substantial contribution to discussion of article content, and reviewed and edited the manuscript before submission. A.M.B.L. and D.N. researched data for the article, and reviewed and edited the manuscript before submission. A.S., R.M. and G.M.H. reviewed and edited the manuscript before submission.

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Correspondence to Laura Ferraiuolo.

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Competing interests

M.A.M. is funded by BenevolentAI. P.N.O., A.M.B.L., D.N., A.S. and J.D.H. work for BenevolentAI. R.M. and L.F. have a project in collaboration with BenevolentAI.

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Nature Reviews Neurology thanks S. Baranzini, D. Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Allen Brain Atlas: http://portal.brain-map.org/

Alzheimer’s Disease Neuroimaging Initiative: http://adni.loni.usc.edu/

Amazon Comprehend Medical initiative: https://aws.amazon.com/comprehend/medical/

Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium: http://enigma.ini.usc.edu/

European Alzheimer’s Disease Consortium Impact of Cholinergic Treatment Use (EADC-ICTUS): http://www.eadc.info/sito/pagine/d_01.php?nav=d

Google TensorFlow: https://www.tensorflow.org/

Parkinson’s Progression Markers Initiative: http://www.ppmi-info.org/

The Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER): http://www.alz.org/wwfingers/overview.asp

UK Biobank: http://www.ukbiobank.ac.uk/

Glossary

Omics

A collective term for a field within biological research concerned with the study of ‘omes’; for example, the genome, transcriptome or proteome.

Endotypes

Clusters of individuals within a disease population that share functional and pathological traits.

Molecular signatures

A collection of proteins, genes and their variants that can be used as hallmarks for a given phenotype.

Regularizing

The technique of adding constraints or knowledge within the training process in order to prevent overfitting.

Data leakage

An undesirable process whereby information is accidentally shared between the training data and the test data, resulting in test evaluation scores that are not representative of real-world unseen data.

Sample-to-feature ratio

(SFR). The number of data points divided by the number of features; for example, gene expression data comprising tens of patients with thousands of gene expression levels would have an SFR of <1.

Overfitting

When an algorithm learns the patterns within the training dataset as opposed to the patterns representative of all data.

Classifier

A type of model used to identify the correct category for a data point.

Hyperparameter

A parameter the value of which is set before training; for example, the attributes of the model architecture.

Cross-validation

A training and evaluation procedure that consists of splitting the data into subsets and alternately holding out one subset for evaluation until all subsets have been evaluated.

Metadata

Data about other data; for example, information about an experimental protocol or the time and date of sample collection.

Genome-wide association study

(GWAS). An observational method of studying genetic variants across a population in search for associations between genetic changes and traits such as diseases.

Next-generation sequencing

High-throughput, deep sequencing of DNA and RNA; this technique utilizes sequencing technologies that are capable of processing multiple DNA or RNA sequences in parallel.

Metabolomics

A study of metabolites, that is, the small molecule substrates, intermediates and products of cellular metabolism, and their interactions within living organisms.

Bayesian inference

A method of statistical inference that uses Bayes’ theorem to calculate the probability of a hypothesis being true on the basis of observed data and prior information.

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Myszczynska, M.A., Ojamies, P.N., Lacoste, A.M.B. et al. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat Rev Neurol 16, 440–456 (2020). https://doi.org/10.1038/s41582-020-0377-8

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