Using machine learning for detection of Parkinson’s disease and mild cognitive impairment

Topic: Risk, prevention and care in population studies

Overall, both machine learning techniques had an equivalent performance. In general, in those models for detecting Parkinson's disease, DaT-SPECT performed better than biofluid biomarkers. In models for detecting Parkinson's disease patients with mild cognitive impairment, combining DaT-SPECT with phosphorylated-tau-181 resulted in higher accuracy, outperforming DaT-SPECT alone. Classification for Parkinson's disease and mild cognitive impairment may be improved by combining neuroimaging with biofluid biomarkers through machine learning models.

Key findings

• Overall, both machine learning techniques had an equivalent performance.
• In general, in those models for detecting Parkinson's disease, DaT-SPECT performed better than biofluid biomarkers.
• In models for detecting Parkinson's disease patients with mild cognitive impairment, combining DaT-SPECT with phosphorylated-tau-181 resulted in higher accuracy, outperforming DaT-SPECT alone.
• Classification for Parkinson's disease and mild cognitive impairment may be improved by combining neuroimaging with biofluid biomarkers through machine learning models.

Why it matters

This work used machine learning to detect Parkinson's disease and mild cognitive impairment, finding that combining DaT-SPECT with phosphorylated-tau-181 improved accuracy over DaT-SPECT alone. It concerns classification and diagnosis rather than treating, curing or preventing Alzheimer's disease.

Study details

• Evidence strengthLow

Read the original

• DOI: 10.1371/journal.pone.0335541
• PubMed: 41259329
• Original source →
• Free full text: PLoS ONE →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{dennis2025-41259329,
  author  = {Dennis, Anthaea-Grace Patricia and Martin, Sarah L. and Chen, Robert E. and Gerretsen, Philip and Strafella, Antonio P.},
  title   = {{Using machine learning for detection of Parkinson’s disease and mild cognitive impairment}},
  journal = {PLoS ONE},
  year    = {2025},
  doi     = {10.1371/journal.pone.0335541},
  pmid    = {41259329},
  url     = {https://doi.org/10.1371/journal.pone.0335541}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment Journal of the American Geriatrics Society · 2005-04-01 · usefulness score 60/100 · Getting a diagnosis
• Parkinson's disease dementia: convergence of α-synuclein, tau and amyloid-β pathologies Nature reviews. Neuroscience · 2013-07-31 · usefulness score 54/100 · Getting a diagnosis

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• Diabetic retinopathy as the primary predictor of mild cognitive impairment in type 2 diabetes: Insights from machine learning models PLoS ONE · 2025-09-26 · usefulness score 51/100

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