Neurology

Patch-Wise Deep Learning Method for Intracranial Stenosis and Aneurysm Detection-the Tromsø Study.

Intracranial atherosclerotic stenosis (ICAS) and intracranial aneurysms are prevalent conditions in the cerebrovascular system. ICAS causes a narrowing of the arterial lumen, thereby restricting blood flow, while aneurysms involve the ballooning of blood vessels. Both conditions can lead to severe outcomes, such as stroke or vessel rupture, which can be fatal. Early detection is crucial for effect...

Automated analysis of spoken language differentiates multiple system atrophy from Parkinson's disease.

BACKGROUND AND OBJECTIVES: Patients with synucleinopathies such as multiple system atrophy (MSA) and Parkinson's disease (PD) frequently display speech and language abnormalities. We explore the diagnostic potential of automated linguistic analysis of natural spontaneous speech to differentiate MSA and PD.

DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer's disease.

Alzheimer's Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, eff...

Screening of Aβ and phosphorylated tau status in the cerebrospinal fluid through machine learning analysis of portable electroencephalography data.

Diagnosing Alzheimer's disease (AD) through pathological markers is typically costly and invasive. This study aims to find a noninvasive, cost-effecti...

Supervised Contrastive Learning-Based Domain Generalization Network for Cross-Subject Motor Decoding.

Developing an electroencephalogram (EEG)-based motor imagery and motor execution (MI/ME) decoding system that is both highly accurate and calibration-...

The global research of magnetic resonance imaging in Alzheimer's disease: a bibliometric analysis from 2004 to 2023.

BACKGROUND: Alzheimer's disease (AD) is a common neurodegenerative disorder worldwide and the using of magnetic resonance imaging (MRI) in the managem...

Differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysis.

The conventional statistical approach for analyzing resting state functional MRI (rs-fMRI) data struggles to accurately distinguish between patients w...

Exploring Dance as a Therapeutic Approach for Parkinson Disease Through the Social Robotics for Active and Healthy Ageing (SI-Robotics): Results From a Technical Feasibility Study.

BACKGROUND: Parkinson disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms. Recently, dance has started to be cons...

NDUFA11 may be the disulfidptosis-related biomarker of ischemic stroke based on integrated bioinformatics, clinical samples, and experimental analyses.

BACKGROUND: Programmed cell death plays an important role in neuronal injury and death after ischemic stroke (IS), leading to cellular glucose deficie...

Drug repurposing using artificial intelligence, molecular docking, and hybrid approaches: A comprehensive review in general diseases vs Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD), the most prevalent form of dementia, remains enigmatic in its origins despite the widely accepted "amyloid hypot...

DML-MFCM: A multimodal fine-grained classification model based on deep metric learning for Alzheimer's disease diagnosis.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder. There are no drugs and methods for the treatment of AD, but early intervention c...

Prognostic value of multi-PLD ASL radiomics in acute ischemic stroke.

INTRODUCTION: Early prognosis prediction of acute ischemic stroke (AIS) can support clinicians in choosing personalized treatment plans. The aim of th...

Current state and promise of user-centered design to harness explainable AI in clinical decision-support systems for patients with CNS tumors.

In neuro-oncology, MR imaging is crucial for obtaining detailed brain images to identify neoplasms, plan treatment, guide surgical intervention, and m...

Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning.

Major depressive disorder (MDD) is associated with substantial morbidity and mortality, yet its diagnosis and treatment rates remain low due to its di...

Clinical validation of artificial intelligence-based single-subject morphometry without normative reference database.

BACKGROUND: Single-subject voxel-based morphometry (VBM) is a powerful technique for reader-independent detection of brain atrophy in structural magne...

Comparative diagnostic accuracy of ChatGPT-4 and machine learning in differentiating spinal tuberculosis and spinal tumors.

BACKGROUND: In clinical practice, distinguishing between spinal tuberculosis (STB) and spinal tumors (ST) poses a significant diagnostic challenge. Th...

Generation of high-resolution MPRAGE-like images from 3D head MRI localizer (AutoAlign Head) images using a deep learning-based model.

PURPOSE: Magnetization prepared rapid gradient echo (MPRAGE) is a useful three-dimensional (3D) T1-weighted sequence, but is not a priority in routine...

A systematic review of deep learning in MRI-based cerebral vascular occlusion-based brain diseases.

Neurological disorders, including cerebral vascular occlusions and strokes, present a major global health challenge due to their high mortality rates ...

Predicting cognitive decline from neuropsychiatric symptoms and Alzheimer's disease biomarkers: A machine learning approach to a population-based data.

BACKGROUND: The aim of this study was to examine the potential added value of including neuropsychiatric symptoms (NPS) in machine learning (ML) model...

Hypothalamic atrophy in primary lateral sclerosis, assessed by convolutional neural network-based automatic segmentation.

Primary lateral sclerosis (PLS) is a motor neuron disease (MND) which mainly affects upper motor neurons. Within the MND spectrum, PLS is much more sl...