Latest AI and machine learning research in parkinson's disease for healthcare professionals.
Protein function prediction traditionally relies on structured gene ontology (GO) labels or multi-label classifiers. However, these labels or classifiers cannot flexibly describe molecular function, biological process, cellular component, and free-text functional narratives in a single output. In comparison, generation-based approaches offer an intuitive paradigm for flexible free-text protein ann...
Parkinson disease (PD) progression is highly heterogeneous. Deeply phenotyped longitudinal research cohorts have enabled characterization of PD progression trajectories. Early prediction of these progression patterns can help us better understand patient disease conditions and manage appropriately. However, the sample sizes of these cohorts are typically too small to build robust early predictors,...
Quantitative gait analysis could enhance personalized treatment for many movement-related conditions; however, it is not routinely integrated into cli...
Background: Motor complications, such as motor fluctuations and Levodopa-induced dyskinesias (LID), significantly impair quality of life in persons wi...
Background: Lewy body diseases (LBD) collectively share alpha-synuclein Lewy pathology, yet present wide clinical heterogeneity, with overlapping moto...
Estimating Parkinson's disease (PD) risk years before diagnosis remains an unmet need. We applied a validated machine learning classifier for REM slee...
Freezing of gait (FoG) in Parkinson's disease is a brief but hazardous gait failure that often precedes falls. For wearable cueing or other closed-loo...
Ground reaction force (GRF)-based gait analysis provides objective, non-invasive evidence for neurological and musculoskeletal assessment, but its tra...
The accessible chemical space dwarfs any tractable screening budget, and most artificial intelligence drug discovery pipelines respond by docking and ...
In industrial environments, new product categories arrive sequentially, requiring continual anomaly detection without access to past data. Normalizing...
Background: Motor neuron disease (MND) is a fatal neurodegenerative condition with significant clinical heterogeneity that is incompletely captured by...
Correspondence is fundamentally relational: it seeks the unknown transformation between two observations of a common scene, not the content of either....
-Synuclein misfolding and progressive accumulation drive a pathogenic process in Parkinson's disease, yet many brain regions develop more or less path...
Reduced facial expressivity is a common motor manifestation of Parkinson's disease (PD), often described as hypomimia or facial bradykinesia. This pap...
Parkinson's disease (PD) is a progressive neurodegenerative disorder that frequently causes speech impairments associated with hypokinetic dysarthria....
Lewy body dementia (LBD), which encompasses Parkinson's disease dementia (PDD) and Dementia with Lewy bodies (DLB), lacks established biofluid markers...
Bone marrow adiposity changes radically through the lifespan, but this phenomenon is poorly characterised and understood in humans. Large datasets of ...
Correlation matrices are fundamental summaries of functional brain networks, yet standard analyses often treat entries independently, ignoring the cur...
Background: Parkinson's disease (PD) has a prolonged prodromal phase during which non-motor symptoms (NMS) may emerge years before the appearance of c...
Background: The clinical applicability of large language models (LLMs) in Parkinson's disease (PD) management remains insufficiently characterized, pa...