Neurology

Metaheuristic-driven machine learning study for early detection and classification of Parkinson's disease using feature prioritization with pelican optimization algorithm.

Parkinson disease (PD) is a degenerative disorder of the brain and afflicts approximately 6 in 10 people aged 50 years or older. PD patients have motor and speech problems, so regular visits to and monitoring of the patients are hard. It is necessary to detect the presence of PD promptly and accurately, since early treatment will contribute greatly to enhancing patients' lives. As the number of ag...

A Physics-Informed Deep Learning Framework for Estimating Muscle Activation Patterns Following Achilles Tendon Repair.

Accurate soleus (SOL) activation assessment is essential for Achilles tendon rupture (ATR) recovery, yet direct measurement remains a clinical challenge. This study proposes a physics-informed Transformer Neuromusculoskeletal Model (NMM) to estimate muscle activations during various locomotor tasks. We evaluated the framework using data from 40 participants, including 20 healthy controls and 20 po...

Deep Learning Assisted Heteroporous Covalent Organic Framework Hydrogels Enable Dual-Mode Tracking and Analysis to Parkinson's Management.

Resting tremor and dopamine dysregulation are closely linked in the pathophysiology of Parkinson's disease (PD), yet detecting their subtle early mani...

Pathology-informed Generative Adversarial Network Augmentation Improves Classification of Peripheral Nerve Sheath Tumors by Modeling Morphological Variability: A Pilot Investigation.

BACKGROUND: Peripheral nerve sheath tumors (PNSTs) of the head and neck (H&N) show histopathological overlap. Although convolutional neural networks (...

Deep learning in acute ischemic stroke imaging: a systematic review of CT- and MRI-based segmentation, triage, and prognostic modeling.

BACKGROUND: Acute ischemic stroke is a time-critical neurological emergency in which imaging directly influences diagnosis, treatment eligibility, tis...

Remote Assessment of Parkinson Disease Using Deep Learning on Structured Mouse-Trace Data From Suspected Cases: Machine-Learning Pilot Feasibility Study.

BACKGROUND: Parkinson disease (PD) is a pervasive neurodegenerative disorder globally, largely characterized by motor symptoms. Most existing artifici...

CastNet: A three-channel EEG-based deep learning model for cross-subject depression detection.

Depression is a serious mental health condition affecting millions worldwide. In recent years, deep learning models achieved remarkable performance in...

Advancing fair and explainable machine learning for neuroimaging dementia pattern classification in multi-racial and multi-ethnic populations.

Dementia, a degenerative disease affecting millions globally, is projected to triple by 2050. Early and precise diagnosis is essential for effective t...

Novel Alzheimer's disease-associated variants and genetic interactions identified from UK biobank whole-exome sequencing data using IBI-DT.

Alzheimer's disease (AD) is a highly heritable neurodegenerative disorder whose genetic architecture remains incompletely understood, particularly wit...

Decoding splicing variants in high-throughput sequencing: a functional validation approach integrating deep learning tools.

High-throughput genome and exome sequencing have uncovered numerous intronic variants in disease genes, yet predicting their impact on pre-mRNA splici...

An Upper-Limb Motor Imagery EEG Dataset of Chronic Stroke Patients.

Motor imagery (MI)-based brain-computer interface (BCI) systems offer a promising approach for post-stroke motor rehabilitation. However, their clinic...

Risk prediction models based on machine learning for emergence delirium in elderly patients undergoing spine surgery: development and validation study.

BACKGROUND: Emergence delirium (ED) is a common complication in elderly patients undergoing surgery for degenerative spinal disease (DSD) and is assoc...

Combining structural and microvascular parameters via machine learning for enhanced diagnosis of normal-tension glaucoma among highly myopic eyes: a prospective cross-sectional diagnostic accuracy study.

PURPOSE: This study aimed to evaluate whether a combination of optical coherence tomography (OCT) and OCT angiography (OCTA) parameters could improve ...

Interpretable machine learning for Parkinson's disease diagnosis, staging, and biological mechanism exploration: a multicenter analysis.

OBJECTIVE: To develop and validate an interpretable machine learning model based on multicenter T1-weighted MRI radiomics data for the three-way class...

A multimodal ConvNeXt-Tiny deep learning model for simultaneous prediction of IDH mutation and Ki-67 expression in gliomas.

OBJECTIVE: To construct and validate a multi-task deep learning model based on ConvNeXt-Tiny for synchronous prediction of isocitrate dehydrogenase (I...

Corneal nerve regeneration: treatments and monitoring approaches.

BACKGROUND: Diagnostics and therapeutics for corneal nerve pathologies are rapidly evolving, with continual advancements in imaging, laser, machine le...

[An electroencephalogram-based emotion recognition method using multi-branch convolutional neural networks and Transformer].

Electroencephalogram (EEG)-based emotion recognition is an important research area in affective computing and mental health assessment. To address the...

[A generalizable epilepsy detection network based on dual-attention mechanism].

Existing deep learning models for epileptic electroencephalogram (EEG) signal analysis frequently overlook intrinsic pathological characteristics duri...

Reinforcement learning-driven adaptive game therapy for cognitive impairment patients with improved vision transformer based detection model.

BACKGROUND: Cognitive impairment is the growing challenge that requires early diagnosis and personalized management of neurodegenerative conditions li...

[The research progress and outlook on the effects of different exercise modalities on working memory function in middle-aged and elderly individuals].

This review provides a comprehensive analysis of the effects of different exercise modalities on working memory function in middle-aged and older adul...