Neurology

Hybrid Transformer for Early Alzheimer's Detection: Integration of Handwriting-Based 2D Images and 1D Signal Features.

Alzheimer's Disease (AD) is a prevalent neurodegenerative condition where early detection is vital. Handwriting, often affected early in AD, offers a non-invasive and cost-effective way to capture subtle motor changes. State-of-the-art research on handwriting, mostly online, based AD detection has predominantly relied on manually extracted features, fed as input to shallow machine learning models....

An EEG-based seizure prediction model encoding brain network temporal dynamics.

EEG-based seizure prediction enables timely treatment for patients, but its performance is limited by the difficulty in effectively characterizing the temporal dynamics of epileptic brain networks. Metastability, which describes recurring topographical patterns of spontaneous neural activity over time, provides a unique perspective for capturing the dynamic evolution before seizure onset. In this ...

Design of a Mobile App and a Clinical Trial Management System for Cognitive Health and Dementia Risk Reduction: User-Centered Design Approach.

BACKGROUND: The rising prevalence of dementia is a major concern, with approximately 45% of cases linked to 14 modifiable risk factors. The European p...

Artificial Intelligence and Machine Learning in Neuromodulation for Epilepsy.

Recent advances in artificial intelligence (AI) and machine learning (ML) can revolutionize neuromodulation therapies for drug-resistant epilepsy. Suc...

Advanced multiscale machine learning for nerve conduction velocity analysis.

This paper presents an advanced machine learning (ML) framework for precise nerve conduction velocity (NCV) analysis, integrating multiscale signal pr...

Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension.

To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing early intracranial hypertension (IH) following primary decompressi...

Machine learning-assisted optimization of dietary intervention against dementia risk.

A healthy diet has been associated with a reduced risk of dementia. Here we devised a Machine learning-assisted Optimizing Dietary intERvention agains...

A deep learning model for early diagnosis of alzheimer's disease combined with 3D CNN and video Swin transformer.

Alzheimer's disease (AD) constitutes a neurodegenerative disorder predominantly observed in the geriatric population. If AD can be diagnosed early, bo...

A dual path graph neural network framework for dementia diagnosis.

Dementia typically results from damage to neural pathways and the consequent degeneration of neuronal connections. Graph neural networks (GNNs) have b...

A multi-modal graph-based framework for Alzheimer's disease detection.

We propose a compositional graph-based Machine Learning (ML) framework for Alzheimer's disease (AD) detection that constructs complex ML predictors fr...

Optimizing the early diagnosis of neurological disorders through the application of machine learning for predictive analytics in medical imaging.

Early diagnosis of Neurological Disorders (ND) such as Alzheimer's disease (AD) and Brain Tumors (BT) can be highly challenging since these diseases c...

Advanced machine learning applications in fibromyalgia to assess the relationship between 3D spinal alignment with clinical outcomes.

This study leveraged machine learning (ML) models to explore the relationship between three-dimensional (3D) spinal alignment parameters and clinical ...

Classifying and diagnosing Alzheimer's disease with deep learning using 6735 brain MRI images.

Traditional diagnostic methods for Alzheimer's disease often suffer from low accuracy and lengthy processing times, delaying crucial interventions and...

A novel neuroimaging based early detection framework for alzheimer disease using deep learning.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive function, posing a major global health chall...

A novel ST-GCN model based on homologous microstate for subject-independent seizure prediction.

Due to the lack of validated universal seizure markers, population-level prediction methods often exhibit limited performance. This study proposes hom...

Scalable geometric learning with correlation-based functional brain networks.

Correlation matrices serve as fundamental representations of functional brain networks in neuroimaging. Conventional analyses often treat pairwise int...

MRI based early Temporal Lobe Epilepsy detection using DGWO based optimized HAETN and Fuzzy-AAL Segmentation Framework (FASF).

This work aims to promote early and accurate diagnosis of Temporal Lobe Epilepsy (TLE) by developing state-of-the-art deep learning techniques, with t...

Soft Robotics for Parkinson's Disease Supported by Functional Materials and Artificial Intelligence.

Progressive neurodegenerative disease known as Parkinson's disease (PD) is characterized by both motor and nonmotor symptoms that severely reduce the ...

Hip surveillance in children with cerebral palsy in the UK : history, challenges, and future directions.

Cerebral palsy (CP) is associated with musculoskeletal complications in children, notably hip migration, which can progress to hip dislocation and joi...

Fully automated measurement of paediatric cerebral palsy pelvic radiographs with BoneFinder : external validation using a national surveillance database.

AIMS: BoneFinder is a machine-learning tool that can automatically calculate Reimers migration percentage (RMP) and head-shaft angle (HSA) from paedia...