Neurology

Enhanced Visualization of Intracranial Cortical Arteries Using Deep Learning Reconstruction in Vessel Wall MR Imaging.

This study evaluated the utility of deep learning reconstruction (DLR) in vessel wall imaging (VWI) for visualizing the entire cerebral arterial system, including cortical arteries. Seventeen patients underwent post-contrast 3D T1WI-CUBE VWI with 0.5 mm isotropic resolution. Images with and without DLR were compared using qualitative and quantitative assessments. Qualitative image quality was rate...

Explainable machine learning algorithm for classifying resting-state functional MRI in amyotrophic lateral sclerosis.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that affects multiple brain systems. Altered brain function can be observed through resting-state functional magnetic resonance imaging (rs-fMRI). While machine learning offers significant advantages in capturing complex signal patterns across numerous voxels, its decision-making process often lacks transparency. ...

IsomiR utility in amyotrophic lateral sclerosis prognostication.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by progressive motor neuron loss. IsomiRs are micro...

Building a Quantitative Telemedicine Platform for Myasthenia Gravis: Augmenting the Physical Examination.

Myasthenia gravis (MG), a fluctuating autoimmune neuromuscular disease, presents unique challenges for remote assessment due to its reliance on detail...

Learning curve for fenestrated-branched endovascular aortic repair using machine learning: A prospective national multicenter registry study.

OBJECTIVE: Fenestrated-branched endovascular aortic repair (F-BEVAR) is a complex procedure that requires significant experience and advanced technica...

A Novel Morlet Convolutional Neural Network.

Automatic seizure detection holds significant importance for epilepsy diagnosis and treatment. Convolutional neural networks (CNNs) have shown immense...

Integration of quantum artificial intelligence in disease diagnosis: A review of methods and applications.

BACKGROUND AND OBJECTIVE: Accurate disease diagnosis is vital for effective treatment and improved patient outcomes. While artificial intelligence (AI...

Identification of the core genes KLRB1 and RETN as potential shared diagnostic markers for major depressive disorder and systemic lupus erythematosus through bioinformatics and machine learning methodologies.

This study investigates the shared molecular mechanisms between major depressive disorder (MDD) and systemic lupus erythematosus (SLE) through integra...

Efficacy of MRI-based deep learning algorithm for detecting acute ischemic stroke: evaluation among diverse readers.

OBJECTIVES: The efficacy of an MRI-based deep learning algorithm (DLA) for detecting acute ischemic stroke (AIS) was evaluated across readers with div...

Ethics, economics, and patient-centered outcomes of spinal cord stimulation.

PURPOSE OF THE REVIEW: With the widespread integration of spinal cord stimulation (SCS) into clinical practice, understanding its ethical, economic, a...

Electroencephalography-Based Machine and Deep Learning Approaches for the Diagnosis of Dissociative Disorders: A Comprehensive Review.

Dissociative disorders (DDs), including dissociative identity disorder and depersonalization disorder, are complex and often misdiagnosed psychiatric ...

Revisiting drunk driving risk among individuals with alcohol use disorder using unsupervised learning: From clinical characteristics and neuropsychological performance to EEG data.

The strong association between alcohol use disorder (AUD) and driving under the influence of alcohol (DUIA) suggests substantial overlaps across the b...

3D printed scaffolds regulated by neural-bone metabolic coupling promote bone unit regeneration.

The bone unit (BU) is a multicellular functional unit composed of neuromodulatory networks, bone tissue, and functional blood vessels. As a functional...

Utility of Baseline Pathological, Neuroimaging and Clinical Markers for Prognosis in Early Parkinson's Disease.

BackgroundCurrently, prognosis of Parkinson's Disease (PD) is limited. Emerging literature highlights potential of multi-modal biomarkers and neuroima...

Resolution and quality enhancement of SPECT cerebral blood flow images using Pix2pix deep learning.

BACKGROUND: Cerebral blood flow (CBF) imaging can be performed using SPECT with 123I-IMP; however, its spatial resolution and image quality are inferi...

Machine learning-assisted high-performance immunoSERS platform using silk fibroin as a natural etching mask for early diagnosis of Alzheimer's disease.

Early and accurate diagnosis of Alzheimer's disease (AD) is a major stride toward pharmacological interventions to delay the onset or progression of t...

Cross-subject emotion recognition with loop adaptive adversarial transfer network.

Emotion recognition has broad application prospects in real life. The variability across subjects in emotion-related electroencephalogram (EEG) signal...

Fuzzy symbolic convergent cross mapping: A causal coupling measure for EEG signals in disorders of consciousness patients.

Accurate and timely diagnosis in disorders of consciousness (DOC) patients remains a core clinical challenge. Electroencephalography (EEG) shows stron...

Robot-assisted gait training for lower limb motor recovery in cerebral palsy: A meta-analysis of combined and standalone approaches.

BACKGROUND: Cerebral palsy (CP), a leading cause of childhood motor disability, severely impacts lower limb function. Conventional gait training (CGT)...

Comparison of RETFound and a Supervised Convolutional Neural Network for Detection of Referable Glaucoma from Fundus Photographs.

PURPOSE: To compare the performance of a vision transformer-based foundation model (RETFound) and a supervised convolutional neural network (VGG-19) f...