Machine learning on Parkinson's disease? Let's translate into clinical practice

Journal of Neuroscience Methods

Volumn 266, Issue , 2016, Pages 161-162

  Cerasa, Antonio ^a  

^a CNR   (Italy)

Author keywords

Clinical practice; Computer based diagnosis; Machine learning; Neuroimaging; Parkinson's disease

Indexed keywords

ALGORITHM; ALZHEIMER DISEASE; CLINICAL PRACTICE; DIAGNOSTIC ACCURACY; DISEASE CLASSIFICATION; HUMAN; MACHINE LEARNING; MOTOR DYSFUNCTION; NEUROIMAGING; NOTE; PARKINSON DISEASE; PRIORITY JOURNAL; PROGRESSIVE SUPRANUCLEAR PALSY; SUPPORT VECTOR MACHINE; DIFFERENTIAL DIAGNOSIS; UNSUPERVISED MACHINE LEARNING;

DIAGNOSIS, DIFFERENTIAL; HUMANS; MACHINE LEARNING; PARKINSON DISEASE; SUPPORT VECTOR MACHINE; UNSUPERVISED MACHINE LEARNING;

EID: 84953432383     PISSN: 01650270     EISSN: 1872678X     Source Type: Journal    
DOI: 10.1016/j.jneumeth.2015.12.005     Document Type: Note

References (11)

1. Adler, C.H., Beach, T.G., Hentz, J.G., Shill, H.A., Caviness, J.N., Driver-Dunckley, E., et al. Low clinical diagnostic accuracy of early vs advanced Parkinson disease: clinicopathologic study. Neurology 83:5 (2014), 406–412, 10.1212/WNL.0000000000000641.
2. Duchesne, S., Rolland, Y., Verin, M., Automated computer differential classification in Parkinsonian syndromes via pattern analysis on MRI. Acad Radiol 16:1 (2009), 61–70, 10.1016/j.acra.2008.05.024.
3. Focke, N.K., Helms, G., Pantel, P.M., Scheewe, S., Knauth, M., Bachmann, C.G., et al. Differentiation of typical and atypical Parkinson syndromes by quantitative MR imaging. AJNR Am J Neuroradiol 32:11 (2011), 2087–2092, 10.3174/ajnr.A2865.
4. Focke, N.K., Helms, G., Scheewe, S., Pantel, P.M., Bachmann, C.G., Dechent, P., et al. Individual voxel-based subtype prediction can differentiate progressive supranuclear palsy from idiopathic Parkinson syndrome and healthy controls. Hum Brain Mapp 32:11 (2011), 1905–1915, 10.1002/hbm.21161.
5. Haller, S., Badoud, S., Nguyen, D., Barnaure, I., Montandon, M.L., Lovblad, K.O., et al. Differentiation between Parkinson disease and other forms of Parkinsonism using support vector machine analysis of susceptibility-weighted imaging (SWI): initial results. Eur Radiol 23:1 (2013), 12–19, 10.1007/s00330-012-2579-y.
6. Haller, S., Badoud, S., Nguyen, D., Garibotto, V., Lovblad, K.O., Burkhard, P.R., Individual detection of patients with Parkinson disease using support vector machine analysis of diffusion tensor imaging data: initial results. AJNR Am J Neuroradiol 33:11 (2012), 2123–2128, 10.3174/ajnr.A3126.
7. Orrù, G., Pettersson-Yeo, W., Marquand, A.F., Sartori, G., Mechelli, A., Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev 36:4 (2012), 1140–1152, 10.1016/j.neubiorev.2012.01.004.
8. Salvatore, C., Cerasa, A., Castiglioni, I., Gallivanone, F., Augimeri, A., Lopez, M., et al. Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and progressive supranuclear palsy. J Neurosci Methods 222 (2014), 230–237, 10.1016/j.jneumeth.2013.11.016.
9. Singh, G., Samavedham, L., Unsupervised learning based feature extraction for differential diagnosis of neurodegenerative diseases: a case study on early-stage diagnosis of Parkinson disease. J Neurosci Methods 256 (2015), 30–40, 10.1016/j.jneumeth.2015.08.011.
10. Wang, S., Summers, R.M., Machine learning radiology. Med Image Anal 16:5 (2012), 933–951, 10.1016/j.media.2012.02.005.
11. Weingarten, C.P., Sundman, M.H., Hickey, P., Chen, N.K., Neuroimaging of Parkinson's disease: expanding views. Neurosci Biobehav Rev, 2015, 10.1016/j.neubiorev.2015.09.007.