Abstract 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.
Tirado et al. (2026) studied this question.