Optimization of cognitive assessment in Parkinsonisms by applying artificial intelligence to a comprehensive screening test

The assessment of cognitive deficits is pivotal for diagnosis and management in patients with parkinsonisms. Low levels of correspondence are observed between evaluations assessed with screening cognitive tests in comparison with those assessed with in-depth neuropsychological batteries. A new tool,...

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Bibliographic Details
Published in:NPJ Parkinson's Disease 2022-04, Vol.8 (1), p.42-9, Article 42
Main Authors: Ortelli, Paola, Ferrazzoli, Davide, Versace, Viviana, Cian, Veronica, Zarucchi, Marianna, Gusmeroli, Anna, Canesi, Margherita, Frazzitta, Giuseppe, Volpe, Daniele, Ricciardi, Lucia, Nardone, Raffaele, Ruffini, Ingrid, Saltuari, Leopold, Sebastianelli, Luca, Baranzini, Daniele, Maestri, Roberto
Format: Article
Language:English
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692/617/375/346/1718
692/700/139/422
Artificial intelligence
Biomedical and Life Sciences
Biomedicine
Brain diseases
Cognition & reasoning
Discriminant analysis
Neurology
Neuropsychology
Neurosciences
Parkinson's disease
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Summary:The assessment of cognitive deficits is pivotal for diagnosis and management in patients with parkinsonisms. Low levels of correspondence are observed between evaluations assessed with screening cognitive tests in comparison with those assessed with in-depth neuropsychological batteries. A new tool, we named CoMDA (Cognition in Movement Disorders Assessment), was composed by merging Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Frontal Assessment Battery (FAB). In total, 500 patients (400 with Parkinson’s disease, 41 with vascular parkinsonism, 31 with progressive supranuclear palsy, and 28 with multiple system atrophy) underwent CoMDA (level 1–L1) and in-depth neuropsychological battery (level 2–L2). Machine learning was developed to classify the CoMDA score and obtain an accurate prediction of the cognitive profile along three different classes: normal cognition (NC), mild cognitive impairment (MCI), and impaired cognition (IC). The classification accuracy of CoMDA, assessed by ROC analysis, was compared with MMSE, MoCA, and FAB. The area under the curve (AUC) of CoMDA was significantly higher than that of MMSE, MoCA and FAB ( p  
ISSN:2373-8057
2373-8057
DOI:10.1038/s41531-022-00304-z