The DOI matches its Crossref record; 205 of the 205 references it cites were found in a registry or an index.
Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature
Open accessAbstract
Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.
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Cites
Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & the PRISMA Group* (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-4-200908180-00135 · Cited by 37,554
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,156
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809
Staging of brain pathology related to sporadic Parkinson’s disease
Braak, H., Tredici, K. D., Rüb, U., de Vos, R. A. I., Jansen Steur, E. N. H., & Braak, E. (2003) · Neurobiology of Aging · doi:10.1016/s0197-4580(02)00065-9 · Cited by 10,777
MDS clinical diagnostic criteria for Parkinson’s disease
Postuma, R. B., Berg, D., Stern, M., Poewe, W., Olanow, C. W., Oertel, W., Obeso, J., Marek, K., Litvan, I., Lang, A. E., et al. (2015) · Movement Disorders · doi:10.1002/mds.26424 · Cited by 7,653
Parkinson's disease: clinical features and diagnosis
Jankovic, J. (2008) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.2007.131045 · Cited by 5,720 · cited 3×
“PD is a progressive neurological disorder associated with motor and non-motor features (Jankovic, 2008) which comprises multiple aspects of movements, including planning, initiation and execution (Contreras-Vidal and Stelmach, 1995).”
Dorsey, E. R., Elbaz, A., Nichols, E., Abbasi, N., Abd-Allah, F., Abdelalim, A., Adsuar, J. C., Ansha, M. G., Brayne, C., Choi, J.-Y. J., et al. (2018) · The Lancet Neurology · doi:10.1016/s1474-4422(18)30295-3 · Cited by 2,816
Epidemiology of Parkinson’s disease
Tysnes, O.-B., & Storstein, A. (2017) · Journal of Neural Transmission · doi:10.1007/s00702-017-1686-y · Cited by 2,414
Machine learning algorithm validation with a limited sample size
Vabalas, A., Gowen, E., Poliakoff, E., & Casson, A. J. (2019) · PLOS ONE · doi:10.1371/journal.pone.0224365 · Cited by 1,901
The Parkinson Progression Marker Initiative (PPMI)
Marek, K., Jennings, D., Lasch, S., Siderowf, A., Tanner, C., Simuni, T., Coffey, C., Kieburtz, K., Flagg, E., Chowdhury, S., et al. (2011) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2011.09.005 · Cited by 1,892
Cited by
Gupta, R., Kumari, S., Senapati, A., Ambasta, R. K., & Kumar, P. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.102013 · Cited by 134
Roussos, G., Herrero, T. R., Hill, D. L., Dowling, A. V., L. T. M. Müller, M., Evers, L. J. W., Burton, J., Derungs, A., Fisher, K., Kilambi, K. P., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00643-4 · Cited by 28
Interpretable video-based tracking and quantification of Parkinsonism clinical motor states
Deng, D., Ostrem, J. L., Nguyen, V., Cummins, D. D., Sun, J., Pathak, A., Little, S., & Abbasi-Asl, R. (2024) · npj Parkinson's Disease · doi:10.1038/s41531-024-00742-x · Cited by 25
Bounsall, K., Milne-Ives, M., Hall, A., Carroll, C., & Meinert, E. (2023) · JMIR Research Protocols · doi:10.2196/46581 · Cited by 16
Metadata
- Authors
- Mei, J., Desrosiers, C., & Frasnelli, J.
- Venue
- Frontiers in Aging Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2021-05-06
- Volume
- 13
- Pages
- 633752-633752
- ISSN
- 1663-4365 (ISSN-L)
- OpenAlex
- W3158559908
- Cited by
- 401
- References
- 205
- FWCI
- 26.89
- Type
- journal article
- Language
- en
- Open access
- gold
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