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Cited by 299Parkinson's Disease Mechanisms and TreatmentsDementia and Cognitive Impairment ResearchArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 37 of the 38 references it cites were found in a registry or an index.

Machine learning on brain MRI data for differential diagnosis of Parkinson’s disease and progressive supranuclear palsy

Salvatore, C., Cerasa, A., Castiglioni, I., Gallivanone, F., Augimeri, A., Lopez, M., Arabia, G., Morelli, M., Gilardi, M.C., Quattrone, A.·2014·Journal of Neuroscience Methods
DOI 10.1016/j.jneumeth.2013.11.016
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Abstract

Background Supervised machine learning has been proposed as a revolutionary approach for identifying sensitive medical image biomarkers (or combination of them) allowing for automatic diagnosis of individual subjects. The aim of this work was to assess the feasibility of a supervised machine learning algorithm for the assisted diagnosis of patients with clinically diagnosed Parkinson's disease (PD) and Progressive Supranuclear Palsy (PSP).Method Morphological T1-weighted Magnetic Resonance Images (MRIs) of PD patients (28), PSP patients (28) and healthy control subjects (28) were used by a supervised machine learning algorithm based on the combination of Principal Components Analysis as feature extraction technique and on Support Vector Machines as classification algorithm. The algorithm was able to obtain voxel-based morphological biomarkers of PD and PSP.Results The algorithm allowed individual diagnosis of PD versus controls, PSP versus controls and PSP versus PD with an Accuracy, Specificity and Sensitivity>90%. Voxels influencing classification between PD and PSP patients involved midbrain, pons, corpus callosum and thalamus, four critical regions known to be strongly involved in the pathophysiological mechanisms of PSP.Comparison with existing methods Classification accuracy of individual PSP patients was consistent with previous manual morphological metrics and with other supervised machine learning application to MRI data, whereas accuracy in the detection of individual PD patients was significantly higher with our classification method.Conclusions The algorithm provides excellent discrimination of PD patients from PSP patients at an individual level, thus encouraging the application of computer-based diagnosis in clinical practice.

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Metadata

Authors
Salvatore, C., Cerasa, A., Castiglioni, I., Gallivanone, F., Augimeri, A., Lopez, M., Arabia, G., Morelli, M., Gilardi, M. C., & Quattrone, A.
Affiliations
  • University of Milano-Bicocca (IT)
  • Institute of Neurological Sciences (IT)
  • Institute of Molecular Bioimaging and Physiology (IT)
  • Magna Graecia University (IT)
Corresponding author
Castiglioni, I.
ORCID
Venue
Journal of Neuroscience Methods
Publisher
Elsevier BV
Published
2014-01
Volume
222
Pages
230-237
ISSN
0165-0270 (ISSN-L)
DOI
10.1016/j.jneumeth.2013.11.016
OpenAlex
W2037984755
Cited by
299
References
38
Citations by year
2026: 15 · 2025: 26 · 2024: 25 · 2023: 36 · 2022: 41 · 2021: 24 · 2020: 37 · 2019: 23 · 2018: 27 · 2017: 24 · 2016: 5 · 2015: 15 · 2014: 1
FWCI
4.45
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Journal of Neuroscience Methods (publishedVersion)
  • PubMed (publishedVersion)
  • BOA (University of Milano-Bicocca) (submittedVersion)
Topics
  • Health Sciences › Medicine › Clinical Neurology › Parkinson's Disease Mechanisms and Treatments
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
Parkinson's disease, progressive supranuclear palsy, differential diagnosis, brain MRI, voxel-based morphometry, computer-aided diagnosis, T1-weighted imaging, support vector machine, principal component analysis, midbrain, thalamus, passive optical networks, corpus callosum
MeSH terms
Aged, Algorithms, Artificial Intelligence, Brain, Corpus Callosum, Diagnosis, Differential, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Mesencephalon, Parkinson Disease, Pons, Retrospective Studies, Sensitivity and Specificity, Supranuclear Palsy, Progressive, Thalamus, Principal Component Analysis, Support Vector Machine

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