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Data-driven clinical decision support tool for diagnosing mild cognitive impairment in Parkinson’s disease
Gabriel Martínez Tirado, Patricia Martins Conde, Stefano Sapienza, Holger Fröhlich, Claire Pauly, Valerie E. Schröder, Sonja Jónsdóttir, Olena Tsurkalenko et al.. npj Parkinson s Disease, 2026-01-12
Topic: Risk, prevention and care in population studies
Parkinson's disease (PD) is a neurodegenerative condition that may affect both motor and cognitive function. Mild cognitive impairment (MCI) is a known risk factor for the progression to dementia in the later stages of the disease. Lengthy and time-consuming neuropsychological assessments, by trained experts, often make MCI diagnosis impractical in routine care. In this context, machine learning (ML) may offer promising support for MCI diagnosis. Thus, we analysed longitudinal data from 115 people with Parkinson's disease (PwPD) and 226 healthy control participants from the Luxembourg Parkinson's Study, combining ML with clinical data to support MCI diagnosis in PwPD. The data-driven model showed a non-inferior performance to the clinical diagnostic reference test (MDS PD-MCI Level II) and identified a subgroup of MCI individuals that was not captured by the clinical test. This finding suggests that ML models can complement clinical assessments, by facilitating the detection of MCI and complementing the diagnostic characterisation of PwPD.
Key findings
- This finding suggests that ML models can complement clinical assessments, by facilitating the detection of MCI and complementing the diagnostic characterisation of PwPD.
Why it matters
This study developed a machine learning model that performed non-inferiorly to clinical assessment for diagnosing mild cognitive impairment in Parkinson's disease and identified a subgroup not captured by the clinical test. It is about improving diagnosis and does not test any treatment, cure, or prevention.
Study details
- Study designlongitudinal study
- Participants115
- Evidence strengthModerate
How this study scored 53/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (npj Parkinson s Disease) |
| Journal's citation record | 1 / 4 | Npj Parkinson s Disease: journal h-index 87 (OpenAlex, 2026-10-29) |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 2 / 4 | 115 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 2 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.0 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | 4 / 6 | Cited 9.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 2 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Myriam Alexandre: h-index 7 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Bonn: institution h-index 1,072 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 10 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41526401) |
| Free to read | 2 / 2 | Free to read |
| Total | 53 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s41531-025-01222-6
- PubMed: 41526401
- Original source →
- Free full text: npj Parkinson s Disease →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Tirado, G. M., Conde, P. M., Sapienza, S., Fröhlich, H., Pauly, C., Schröder, V. E., et al. (2026). Data-driven clinical decision support tool for diagnosing mild cognitive impairment in Parkinson’s disease. npj Parkinson s Disease. https://doi.org/10.1038/s41531-025-01222-6
Vancouver
Tirado GM, Conde PM, Sapienza S, Fröhlich H, Pauly C, Schröder VE, et al. Data-driven clinical decision support tool for diagnosing mild cognitive impairment in Parkinson’s disease. npj Parkinson s Disease. 2026. doi:10.1038/s41531-025-01222-6. PMID: 41526401.
BibTeX
@article{tirado2026-41526401,
author = {Tirado, Gabriel Martínez and Conde, Patricia Martins and Sapienza, Stefano and Fröhlich, Holger and Pauly, Claire and Schröder, Valerie E. and Jónsdóttir, Sonja and Tsurkalenko, Olena and others},
title = {{Data-driven clinical decision support tool for diagnosing mild cognitive impairment in Parkinson’s disease}},
journal = {npj Parkinson s Disease},
year = {2026},
doi = {10.1038/s41531-025-01222-6},
pmid = {41526401},
url = {https://doi.org/10.1038/s41531-025-01222-6}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here. Showing 12 of 18.