Neurology

Differentiating atypical parkinsonian syndromes with hyperbolic few-shot contrastive learning.

Differences in iron accumulation patterns have been observed in susceptibility-weighted images across different classes of atypical parkinsonian syndromes (APS). Deep learning methods have shown great potential in automatically detecting these differences. However, the models typically require extensively labeled training datasets, which are costly and pose patient privacy risks. To address the is...

Identifying shared diagnostic genes and mechanisms in vascular dementia and Alzheimer's disease via bioinformatics and machine learning.

BACKGROUND: Alzheimer's disease (AD) and vascular dementia (VaD) share overlapping pathophysiological characteristics, yet comparative genetic studies are rare. Understanding these overlaps may aid in identifying common diagnostic markers and therapeutic targets.

Single-channel electroencephalography decomposition by detector-atom network and its pre-trained model.

Signal decomposition techniques utilizing multi-channel spatial features are critical for analyzing, denoising, and classifying electroencephalography...

Neural correlates of empathy in donation decisions: Insights from EEG and machine learning.

Empathy is central to individual and societal well-being. Numerous studies have examined how trait of empathy affects prosocial behavior. However, lit...

Generative modeling of the Circle of Willis using 3D-StyleGAN.

The circle of Willis (CoW) is a network of cerebral arteries with significant inter-individual anatomical variations. Deep learning has been used to c...

Deep learning-based denoising for unbiased analysis of morphology and stiffness in amyloid fibrils.

Understanding the morphology of amyloid fibrils is crucial for comprehending the aggregation and degradation mechanisms of abnormal proteins implicate...

Artificial Intelligence to Diagnose Complications of Diabetes.

Artificial intelligence (AI) is increasingly being used to diagnose complications of diabetes. Artificial intelligence is technology that enables comp...

Multi-scale multimodal deep learning framework for Alzheimer's disease diagnosis.

Multimodal neuroimaging data, including magnetic resonance imaging (MRI) and positron emission tomography (PET), provides complementary information ab...

Establishing a machine learning dementia progression prediction model with multiple integrated data.

OBJECTIVE: Dementia is a significant medical and social issue in most developed countries. Practical tools for predicting the progression of degenerat...

Decoding Glioblastoma Heterogeneity: Neuroimaging Meets Machine Learning.

Recent advancements in neuroimaging and machine learning have significantly improved our ability to diagnose and categorize isocitrate dehydrogenase (...

Effectiveness of Comprehensive Video Datasets: Toward the Development of an Artificial Intelligence Model for Ultrasonography-Based Severity Diagnosis of Carpal Tunnel Syndrome.

OBJECTIVES: Advances in diagnosing carpal tunnel syndrome (CTS) using ultrasonography (US) and artificial intelligence (AI) aim to replace nerve condu...

Automatic discrimination between neuroendocrine carcinomas and grade 3 neuroendocrine tumors by deep learning of H&E images.

Neuroendocrine neoplasms (NENs) arise from diffuse neuroendocrine cells and are categorized as either well-differentiated and less proliferative Neuro...

A novel approach for brain connectivity using recurrent neural networks and integrated gradients.

Brain connectivity is an important tool for understanding the cognitive and perceptive neural mechanisms in the neuroimaging field. Many methods for e...

Learning a Hand Model From Dynamic Movements Using High-Density EMG and Convolutional Neural Networks.

OBJECTIVE: Surface electromyography (sEMG) can sense the motor commands transmitted to the muscles. This work presents a deep learning method that can...

FDCN-C: A deep learning model based on frequency enhancement, deformable convolution network, and crop module for electroencephalography motor imagery classification.

Motor imagery (MI)-electroencephalography (EEG) decoding plays an important role in brain-computer interface (BCI), which enables motor-disabled patie...

Parkinson's disease prediction using improved crayfish optimization based hybrid deep learning.

BackgroundPredicting the course of Parkinson's disease is essential for prompt diagnosis and treatment, which may enhance patient outcomes.ObjectiveTh...

Development and Assessment of an AI-based Machine Learning Model for Predicting Urinary Continence and Erectile Function Recovery after Robotic-Assisted Radical Prostatectomy: Insights from a Prostate Cancer Referral Center.

INTRODUCTION: Prostate cancer remains a significant health concern, with radical prostatectomy being a common treatment approach. However, predicting ...

Prediction of Survival After Pediatric Cardiac Arrest Using Quantitative EEG and Machine Learning Techniques.

BACKGROUND AND OBJECTIVES: Early neuroprognostication in children with reduced consciousness after cardiac arrest (CA) is a major clinical challenge. ...

Wearable EEG Neurofeedback Based-on Machine Learning Algorithms for Children with Autism: A Randomized, Placebo-controlled Study.

OBJECTIVE: Behavioral interventions have been shown to ameliorate the electroencephalogram (EEG) dynamics underlying the behavioral symptoms of autism...

Classification of hand movements from EEG using a FusionNet based LSTM network.

. Accurate classification of electroencephalogram (EEG) signals is crucial for advancing brain-computer interface (BCI) technology. However, current m...