Neurology

Dementia

Latest AI and machine learning research in dementia for healthcare professionals.

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Identification of UBE2N as a biomarker of Alzheimer's disease by combining WGCNA with machine learning algorithms.

Alzheimer's disease (AD) is the most common cause of dementia, emphasizing the critical need for the...

Integrating NLP and LLMs to discover biomarkers and mechanisms in Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurological condition characterized by cognitive decline,...

Enhanced in silico QSAR-based screening of butyrylcholinesterase inhibitors using multi-feature selection and machine learning.

Butyrylcholinesterase inhibition offers one of the formulated solutions to tackle the aggravating sy...

Towards realistic simulation of disease progression in the visual cortex with CNNs.

Convolutional neural networks (CNNs) and mammalian visual systems share architectural and informatio...

Artificial intelligence-powered 3D analysis of video-based caregiver-child interactions.

We introduce HARMONI, a three-dimensional (3D) computer vision and audio processing method for analy...

Dense convolution-based attention network for Alzheimer's disease classification.

Recently, deep learning-based medical image classification models have made substantial advancements...

Stacked CNN-based multichannel attention networks for Alzheimer disease detection.

Alzheimer's Disease (AD) is a progressive condition of a neurological brain disorder recognized by s...

Boostering diagnosis of frontotemporal lobar degeneration with AI-driven neuroimaging - A systematic review and meta-analysis.

BACKGROUND AND OBJECTIVES: Frontotemporal lobar degeneration (FTLD) as the second most common dement...

Cultural variation in trust and acceptability of artificial intelligence diagnostics for dementia.

Digital health innovations hold diagnostic and therapeutic promise but may be subject to biases for ...

Insights from the eyes: a systematic review and meta-analysis of the intersection between eye-tracking and artificial intelligence in dementia.

OBJECTIVES: Dementia can change oculomotor behavior, which is detectable through eye-tracking. This ...

Reducing inference cost of Alzheimer's disease identification using an uncertainty-aware ensemble of uni-modal and multi-modal learners.

While multi-modal deep learning approaches trained using magnetic resonance imaging (MRI) and fluoro...

Hybrid-RViT: Hybridizing ResNet-50 and Vision Transformer for Enhanced Alzheimer's disease detection.

Alzheimer's disease (AD) is a leading cause of disability worldwide. Early detection is critical for...

Taylor-dingo optimized RP-net for segmentation toward Alzheimer's disease detection and classification using deep learning.

Alzheimer's Disease (AD) is a significant cause of mortality in elderly people. The diagnosing and c...

A quantitatively interpretable model for Alzheimer's disease prediction using deep counterfactuals.

Deep learning (DL) for predicting Alzheimer's disease (AD) has provided timely intervention in disea...

Prediction of cognitive conversion within the Alzheimer's disease continuum using deep learning.

BACKGROUND: Early diagnosis and accurate prognosis of cognitive decline in Alzheimer's disease (AD) ...

Alzheimer's Disease detection and classification using optimized neural network.

Alzheimer's disease (AD) is a degenerative neurological condition characterized by a progressive dec...

Alzheimer's disease classification using hybrid loss Psi-Net segmentation and a new hybrid network model.

Alzheimer's disease (AD) is a type of brain disorder that is becoming more prevalent worldwide. It i...

EEGConvNeXt: A novel convolutional neural network model for automated detection of Alzheimer's Disease and Frontotemporal Dementia using EEG signals.

BACKGROUND AND OBJECTIVE: Deep learning models have gained widespread adoption in healthcare for acc...

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