AIMC Topic: Parkinson Disease

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Multi-Level Ethical Considerations of Artificial Intelligence Health Monitoring for People Living with Parkinson's Disease.

AJOB empirical bioethics
Artificial intelligence (AI) has garnered tremendous attention in health care, and many hope that AI can enhance our health system's ability to care for people with chronic and degenerative conditions, including Parkinson's Disease (PD). This paper r...

Protocol to implement a computational pipeline for biomedical discovery based on a biomedical knowledge graph.

STAR protocols
Biomedical knowledge graphs (BKGs) provide a new paradigm for managing abundant biomedical knowledge efficiently. Today's artificial intelligence techniques enable mining BKGs to discover new knowledge. Here, we present a protocol for implementing a ...

Liquid-biopsy proteomics combined with AI identifies cellular drivers of eye aging and disease in vivo.

Cell
Single-cell analysis in living humans is essential for understanding disease mechanisms, but it is impractical in non-regenerative organs, such as the eye and brain, because tissue biopsies would cause serious damage. We resolve this problem by integ...

The role of robot-assisted training on rehabilitation outcomes in Parkinson's disease: a systematic review and meta-analysis.

Disability and rehabilitation
PURPOSE: The study aims to assess the efficacy of robot-assisted rehabilitation training on upper and lower limb motor function and fatigue in Parkinson's disease (PD), and to explore the best-acting robotic rehabilitation program.

Unsupervised anomaly detection by densely contrastive learning for time series data.

Neural networks : the official journal of the International Neural Network Society
Time series data continuously collected by different sensors play an essential role in monitoring and predicting events in many real-world applications, and anomaly detection for time series has received increasing attention during the past decades. ...

Deep-learning detection of mild cognitive impairment from sleep electroencephalography for patients with Parkinson's disease.

PloS one
Parkinson's disease which is the second most prevalent neurodegenerative disorder in the United States is a serious and complex disease that may progress to mild cognitive impairment and dementia. The early detection of the mild cognitive impairment ...

Externally validated deep learning model to identify prodromal Parkinson's disease from electrocardiogram.

Scientific reports
Little is known about electrocardiogram (ECG) markers of Parkinson's disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the...

Probing the molecular mechanisms of α-synuclein inhibitors unveils promising natural candidates through machine-learning QSAR, pharmacophore modeling, and molecular dynamics simulations.

Molecular diversity
Parkinson's disease is characterized by a multifactorial nature that is linked to different pathways. Among them, the abnormal deposition and accumulation of α-synuclein fibrils is considered a neuropathological hallmark of Parkinson's disease. Sever...

New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease.

Ageing research reviews
Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction ...