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Neurodegenerative Diseases

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PPAD: a deep learning architecture to predict progression of Alzheimer's disease.

Bioinformatics (Oxford, England)
MOTIVATION: Alzheimer's disease (AD) is a neurodegenerative disease that affects millions of people worldwide. Mild cognitive impairment (MCI) is an intermediary stage between cognitively normal state and AD. Not all people who have MCI convert to AD...

Classification and deep-learning-based prediction of Alzheimer disease subtypes by using genomic data.

Translational psychiatry
Late-onset Alzheimer's disease (LOAD) is the most common multifactorial neurodegenerative disease among elderly people. LOAD is heterogeneous, and the symptoms vary among patients. Genome-wide association studies (GWAS) have identified genetic risk f...

Deep Learning-Assisted Gait Parameter Assessment for Neurodegenerative Diseases: Model Development and Validation.

Journal of medical Internet research
BACKGROUND: Neurodegenerative diseases (NDDs) are prevalent among older adults worldwide. Early diagnosis of NDD is challenging yet crucial. Gait status has been identified as an indicator of early-stage NDD changes and can play a significant role in...

Evaluating the performance of large language models: ChatGPT and Google Bard in generating differential diagnoses in clinicopathological conferences of neurodegenerative disorders.

Brain pathology (Zurich, Switzerland)
This study explores the utility of the large language models (LLMs), specifically ChatGPT and Google Bard, in predicting neuropathologic diagnoses from clinical summaries. A total of 25 cases of neurodegenerative disorders presented at Mayo Clinic br...

CT-based volumetric measures obtained through deep learning: Association with biomarkers of neurodegeneration.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Cranial computed tomography (CT) is an affordable and widely available imaging modality that is used to assess structural abnormalities, but not to quantify neurodegeneration. Previously we developed a deep-learning-based model that pro...

Retina Oculomics in Neurodegenerative Disease.

Annals of biomedical engineering
Ophthalmic biomarkers have long played a critical role in diagnosing and managing ocular diseases. Oculomics has emerged as a field that utilizes ocular imaging biomarkers to provide insights into systemic diseases. Advances in diagnostic and imaging...

Speech and language processing with deep learning for dementia diagnosis: A systematic review.

Psychiatry research
Dementia is a progressive neurodegenerative disease that burdens the person living with the disease, their families, and medical and social services. Timely diagnosis of dementia could be followed by introducing interventions that may slow down its p...

Human Motor System-Based Biohybrid Robot-On-a-Chip for Drug Evaluation of Neurodegenerative Disease.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Biohybrid robots have been developed for biomedical applications and industrial robotics. However, the biohybrid robots have limitations to be applied in neurodegenerative disease research due to the absence of a central nervous system. In addition, ...

A deep learning model for Alzheimer's disease diagnosis based on patient clinical records.

Computers in biology and medicine
BACKGROUND: Dementia, with Alzheimer's disease (AD) being the most common type of this neurodegenerative disease, is an under-diagnosed health problem in older people. The creation of classification models based on AD risk factors using Deep Learning...

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...