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Neurodegenerative Diseases

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Improving well-being in patients with major neurodegenerative disorders: differential efficacy of brief social robot-based intervention for 3 neuropsychiatric profiles.

Clinical interventions in aging
BACKGROUND: Behavioral and psychological symptoms of dementia (BPSD) affect patients' daily life and subjective well-being. International recommendations stress nonpharmacological interventions as first-line treatment. While newer psychosocial initia...

Modeling cognitive deficits following neurodegenerative diseases and traumatic brain injuries with deep convolutional neural networks.

Brain and cognition
The accurate diagnosis and assessment of neurodegenerative disease and traumatic brain injuries (TBI) remain open challenges. Both cause cognitive and functional deficits due to focal axonal swellings (FAS), but it is difficult to deliver a prognosis...

Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises.

IEEE transactions on pattern analysis and machine intelligence
Discriminative methods commonly produce models with relatively good generalization abilities. However, this advantage is challenged in real-world applications (e.g., medical image analysis problems), in which there often exist outlier data points (sa...

CaspNeuroD: a knowledgebase of predicted caspase cleavage sites in human proteins related to neurodegenerative diseases.

Database : the journal of biological databases and curation
BACKGROUND: A variety of neurodegenerative diseases (NDs) have been associated with deregulated caspase activation that leads to neuronal death. Caspases appear to be involved in the molecular pathology of NDs by directly cleaving important proteins....

Neurodegenerative disease: MRI biomarkers - a precision medicine tool in neurology?

Nature reviews. Neurology
Two new studies highlight the potential of neuroimaging to aid the differential diagnosis of neurodegenerative disease, for both clinical practice and emerging trials. Although this approach holds great promise, meaningful implementation of neuroimag...

Extended Technical and Clinical Validation of Deep Learning-Based Brainstem Segmentation for Application in Neurodegenerative Diseases.

Human brain mapping
Disorders of the central nervous system, including neurodegenerative diseases, frequently affect the brainstem and can present with focal atrophy. This study aimed to (1) optimize deep learning-based brainstem segmentation for a wide range of patholo...

Evaluating the efficacy of few-shot learning for GPT-4Vision in neurodegenerative disease histopathology: A comparative analysis with convolutional neural network model.

Neuropathology and applied neurobiology
AIMS: Recent advances in artificial intelligence, particularly with large language models like GPT-4Vision (GPT-4V)-a derivative feature of ChatGPT-have expanded the potential for medical image interpretation. This study evaluates the accuracy of GPT...

Deep Learning and Machine Learning Algorithms for Retinal Image Analysis in Neurodegenerative Disease: Systematic Review of Datasets and Models.

Translational vision science & technology
PURPOSE: Retinal images contain rich biomarker information for neurodegenerative disease. Recently, deep learning models have been used for automated neurodegenerative disease diagnosis and risk prediction using retinal images with good results.

Artificial Intelligence in The Management of Neurodegenerative Disorders.

CNS & neurological disorders drug targets
Neurodegenerative disorders are characterized by a gradual but irreversible loss of neurological function. The ability to detect and treat these conditions successfully is crucial for ensuring the best possible quality of life for people who suffer f...