Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
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.
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Machine learning has the potential to enhance the accuracy and efficiency of dementia diagnosis.
“Machine learning (ML) has the potential to enhance the accuracy and efficiency of dementia diagnosis26–28.”
From AI-based differential diagnosis of dementia etiologies on multimodal data (2024) · cites this paper as [27]
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
The potential for artificial intelligence in healthcare
Davenport, T., & Kalakota, R. (2019) · Future Healthcare Journal · doi:10.7861/futurehosp.6-2-94 · Cited by 3,800 · see citing works
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
Ordóñez, F., & Roggen, D. (2016) · Sensors · doi:10.3390/s16010115 · Cited by 2,761 · see citing works
Genomics is failing on diversity
Popejoy, A. B., & Fullerton, S. M. (2016) · Nature · doi:10.1038/538161a · Cited by 1,924 · see citing works
Alzheimer’s disease drug-development pipeline: Few candidates, frequent failures
Cummings, J. L., Morstorf, T., & Zhong, K. (2014) · Alzheimer's Research & Therapy · doi:10.1186/alzrt269 · Cited by 1,744 · see citing works
Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study
Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N. G., Venugopal, V. K., Mahajan, V., Rao, P., & Warier, P. (2018) · The Lancet · doi:10.1016/s0140-6736(18)31645-3 · Cited by 1,020 · see citing works
Object Recognition with Gradient-Based Learning
LeCun, Y., Haffner, P., Bottou, L., & Bengio, Y. (1999) · Lecture Notes in Computer Science · doi:10.1007/3-540-46805-6_19 · Cited by 947 · see citing works
Gur, R. C., Turetsky, B. I., Matsui, M., Yan, M., Bilker, W., Hughett, P., & Gur, R. E. (1999) · The Journal of Neuroscience · doi:10.1523/jneurosci.19-10-04065.1999 · Cited by 909 · see citing works
Molecular pathways of motor neuron injury in amyotrophic lateral sclerosis
Ferraiuolo, L., Kirby, J., Grierson, A. J., Sendtner, M., & Shaw, P. J. (2011) · Nature Reviews Neurology · doi:10.1038/nrneurol.2011.152 · Cited by 615 · see citing works
Self-citationGenotype Influences In Vivo Dopamine Transporter Availability in Human Striatum
Heinz, A. (2000) · Neuropsychopharmacology · doi:10.1016/s0893-133x(99)00099-8 · Cited by 538 · see citing works
Young, J., Modat, M., Cardoso, M. J., Mendelson, A., Cash, D., & Ourselin, S. (2013) · NeuroImage Clinical · doi:10.1016/j.nicl.2013.05.004 · Cited by 249 · see citing works
Cerebral correlates of psychotic symptoms in Alzheimer's disease
Mega, M. S. (2000) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.69.2.167 · Cited by 189 · see citing works
Haure-Mirande, J.-V., Audrain, M., Fanutza, T., Kim, S. H., Klein, W. L., Glabe, C., Readhead, B., Dudley, J. T., Blitzer, R. D., Wang, M., et al. (2017) · Acta Neuropathologica · doi:10.1007/s00401-017-1737-3 · Cited by 127 · see citing works
Detecting Dementia Through Interactive Computer Avatars
Tanaka, H., Adachi, H., Ukita, N., Ikeda, M., Kazui, H., Kudo, T., & Nakamura, S. (2017) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2017.2752152 · Cited by 127 · see citing works
Identifying Neuroimaging Markers of Motor Disability in Acute Stroke by Machine Learning Techniques
Rehme, A. K., Volz, L. J., Feis, D.-L., Bomilcar-Focke, I., Liebig, T., Eickhoff, S. B., Fink, G. R., & Grefkes, C. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhu100 · Cited by 117 · see citing works
Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging
Kennedy, D. N., Abraham, S. A., Bates, J. F., Crowley, A., Ghosh, S., Gillespie, T., Goncalves, M., Grethe, J. S., Halchenko, Y. O., Hanke, M., et al. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00001 · Cited by 113 · see citing works
Mobile and pervasive computing technologies and the future of Alzheimer’s clinical trials
Doraiswamy, P. M., Narayan, V. A., & Manji, H. K. (2018) · npj Digital Medicine · doi:10.1038/s41746-017-0008-y · Cited by 97 · see citing works
The Syndrome of Functional Memory Disorder: Definition, Etiology, and Natural Course
Schmidtke, K., Pohlmann, S., & Metternich, B. (2008) · The American Journal of Geriatric Psychiatry · doi:10.1097/jgp.0b013e318187ddf9 · Cited by 91 · see citing works
Haure-Mirande, J.-V., Wang, M., Audrain, M., Fanutza, T., Kim, S. H., Heja, S., Readhead, B., Dudley, J. T., Blitzer, R. D., Schadt, E. E., et al. (2019) · Molecular Psychiatry · doi:10.1038/s41380-018-0255-6 · Cited by 84 · see citing works
A health app developer’s guide to law and policy: a multi-sector policy analysis
Parker, L., Karliychuk, T., Gillies, D., Mintzes, B., Raven, M., & Grundy, Q. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0535-0 · Cited by 71 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Revolutionizing healthcare: the role of artificial intelligence in clinical practice
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., et al. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04698-z · Cited by 3,146 · see citing works
Mead, R. J., Shan, N., Reiser, H. J., Marshall, F., & Shaw, P. J. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-022-00612-2 · Cited by 505 · see citing works
Self-citationSingle-cell and spatial transcriptomics: deciphering brain complexity in health and disease
Piwecka, M., Rajewsky, N., & Rybak-Wolf, A. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00809-y · Cited by 320 · see citing works
The human connectome in Alzheimer disease — relationship to biomarkers and genetics
Yu, M., Sporns, O., & Saykin, A. J. (2021) · Nature Reviews Neurology · doi:10.1038/s41582-021-00529-1 · Cited by 265 · see citing works
AI-based differential diagnosis of dementia etiologies on multimodal data
Xue, C., Kowshik, S. S., Lteif, D., Puducheri, S., Jasodanand, V. H., Zhou, O. T., Walia, A. S., Guney, O. B., Zhang, J. D., Poésy, S., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03118-z · Cited by 182 · see citing works
Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia
Winchester, L. M., Harshfield, E. L., Shi, L., Badhwar, A., Khleifat, A. A., Clarke, N., Dehsarvi, A., Lengyel, I., Lourida, I., Madan, C. R., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13390 · Cited by 130 · see citing works
Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review
Borchert, R. J., Azevedo, T., Badhwar, A., Bernal, J., Betts, M., Bruffaerts, R., Burkhart, M. C., Dewachter, I., Gellersen, H. M., Low, A., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13412 · Cited by 125 · see citing works
Avuçlu, E., & Elen, A. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02260-3 · Cited by 74 · see citing works
Bernal, J., & Mazo, C. (2022) · Applied Sciences · doi:10.3390/app122010228 · Cited by 70 · see citing works
Machine learning in Alzheimer’s disease drug discovery and target identification
Geng, C., Wang, Z., & Tang, Y. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2023.102172 · Cited by 34 · see citing works
Extracellular vesicles as nanotheranostic platforms for targeted neurological disorder interventions
Choi, H. K., Chen, M., Goldston, L. L., & Lee, K.-B. (2024) · Nano Convergence · doi:10.1186/s40580-024-00426-5 · Cited by 26 · see citing works
Folguera-Blasco, N., Boshier, F. A. T., Uatay, A., Pichardo-Almarza, C., Lai, M., Biasetti, J., Dearden, R., Gibbs, M., & Kimko, H. (2024) · Frontiers in Systems Biology · doi:10.3389/fsysb.2024.1380685 · Cited by 15 · see citing works
Towards an AI biomedical scientist: Accelerating discoveries in neurodegenerative disease
Roberts, K. F., Landsness, E. C., Reese, J., Elbert, D., Strobel, G., Wu, E., Chen, Y., Lai, A., Abrams, Z. B., Zhu, M., et al. (2026) · The Journal of Prevention of Alzheimer's Disease · doi:10.1016/j.tjpad.2025.100398 · Cited by 1 · see citing works
- Authors
- Myszczynska, M. A., Ojamies, P. N., Lacoste, A. M. B., Neil, D., Saffari, A., Mead, R., Hautbergue, G. M., Holbrook, J. D., & Ferraiuolo, L.
- Affiliations
- University of Sheffield (GB)
- BenevolentAI (United Kingdom) (GB)
- Corresponding author
- Ferraiuolo, Laura
- ORCID
- Venue
- Nature Reviews Neurology
- Published
- 2020-07-15
- Volume
- 16
- Issue
- 8
- Pages
- 440-456
- ISSN
- 1759-4758 (ISSN-L)
- 1759-4766
- OpenAlex
- W3043374725
- Cited by
- 595
- References
- 22
- Citations by year
- 2026: 58 · 2025: 131 · 2024: 135 · 2023: 118 · 2022: 93 · 2021: 54 · 2020: 6
- FWCI
- 5.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Reviews Neurology (publishedVersion)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Funders
- Motor Neurone Disease Association (SITraN/Apr13/983-797)
- Academy of Medical Sciences (SBF002\1142)
- Medical Research Council (MR/R024162/1)
- Biotechnology and Biological Sciences Research Council (BB/S005277/1)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- machine learning, neurodegenerative diseases, early diagnosis, medical image interpretation, treatment development, prognosis, multi-omics integration, medical data analysis, artificial intelligence in healthcare
- MeSH terms
- Machine Learning, Humans, Neurodegenerative Diseases, Neuroimaging
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