Communications medicine · 2026
Assessment of mental and behavioural non-motor symptoms of Parkinson's Disease using Artificial Intelligence (AI): a systematic review.
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Chou Shantao Chloe, Cong Cen, Brownson-Smith Rosiered, Milne-Ives Madison, Meinert Edward
Аннотация
BACKGROUND: Parkinson's disease is a progressive neurodegenerative disorder with both motor and non-motor symptoms. Mental and behavioural non-motor symptoms such as cognitive impairment, sleep disturbances, depression, and anxiety greatly affect quality of life but remain difficult to assess with traditional tools. Artificial intelligence has shown potential in healthcare, yet its role in evaluating these symptoms in Parkinson's disease remains under-reviewed. This systematic review aims to evaluate the performance of artificial intelligence tools in diagnosing, assessing, and managing these symptoms.
METHODS: Five databases (Medline, Embase, Scopus, Web of Science and PubMed) were searched up to June 2024 for peer-reviewed studies applying artificial intelligence to mental or behavioural symptoms in adults with Parkinson's disease. Studies published before 2010 or lacking artificial-intelligence technologies were excluded. Study quality and risk of bias were assessed using QUADAS-2. Extracted data include study objectives, data sources, algorithms, best model, and diagnostic performance (accuracy, sensitivity, specificity). The study received no external financial support.
RESULTS: Here we show sixteen studies examine cognitive impairment and seven examine sleep disorders. However, only three studies focus on depression and one on anxiety, revealing a research gap. No meta-analysis was performed due to heterogeneity.
CONCLUSIONS: Artificial intelligence shows promise for assessing mental and behavioural symptoms in Parkinson's disease, particularly cognitive and sleep disorders. Multimodal models demonstrate higher accuracy than single-source models, though external validation is necessary. The limited studies on depression and anxiety reflect existing diagnostic challenges and data limitations. Future research should refine diagnostic tools and expand multimodal approaches to these symptoms.
METHODS: Five databases (Medline, Embase, Scopus, Web of Science and PubMed) were searched up to June 2024 for peer-reviewed studies applying artificial intelligence to mental or behavioural symptoms in adults with Parkinson's disease. Studies published before 2010 or lacking artificial-intelligence technologies were excluded. Study quality and risk of bias were assessed using QUADAS-2. Extracted data include study objectives, data sources, algorithms, best model, and diagnostic performance (accuracy, sensitivity, specificity). The study received no external financial support.
RESULTS: Here we show sixteen studies examine cognitive impairment and seven examine sleep disorders. However, only three studies focus on depression and one on anxiety, revealing a research gap. No meta-analysis was performed due to heterogeneity.
CONCLUSIONS: Artificial intelligence shows promise for assessing mental and behavioural symptoms in Parkinson's disease, particularly cognitive and sleep disorders. Multimodal models demonstrate higher accuracy than single-source models, though external validation is necessary. The limited studies on depression and anxiety reflect existing diagnostic challenges and data limitations. Future research should refine diagnostic tools and expand multimodal approaches to these symptoms.