Neurology

TriGA-Net: A graph attention network for brain-controlled speaker extraction.

OBJECTIVE: Electroencephalography (EEG)-guided target speaker extraction aims to recover the listener's attended speech from mixed speech. However, effectively representing and integrating the target-related information shared between EEG and speech remains challenging. This study focuses on this challenge. APPROACH: We propose TriGA-Net, a Graph Attention Network for Brain-Controlled Speaker Extr...

Manifold representation learning for Alzheimer's disease detection with EEG-based functional brain connectivity.

Alzheimer's disease (AD) is a common neurodegenerative disease characterized by severe cognitive dysfunctions and brain disorders. The emergence of deep learning methods provides a feasible way to develop effective representations for clinical diagnosis of brain diseases. In this study, we proposed a deep manifold representation learning network to characterize the alteration of functional brain c...

Research Progress of Diabetes-related Glaucoma: Mechanisms, Impacts and Management Strategies.

Diabetes mellitus (DM), a highly prevalent metabolic disorder, is increasingly recognised for its significant association with glaucoma, a leading cau...

The Imaging Compass: Navigating Reperfusion in Acute Ischemic Stroke.

The management of acute ischemic stroke has shifted from rigid time-based protocols to imaging-driven, tissue-based reperfusion strategies. Non-contra...

Clinical validation pipeline of a deep learning model for segmenting and quantifying intracranial and ventricular volumes on computed tomography.

The field of radiology is experiencing a surge in demand due to advances in medical imaging, particularly in techniques such as magnetic resonance ima...

Saliva Liquid Biopsy for Detection of Oral and Systemic Diseases.

Saliva has emerged as a compelling liquid biopsy for the non-invasive diagnosis and monitoring of both oral and systemic diseases. Secreted by major a...

Pathogenic Genetic Variants, Comorbid Autism and Adaptive Developmental Quotient as Independent Predictors of Intellectual Disability in Children With Global Developmental Delay: An Interpretable Machine Learning Model With Calibrated Risk Estimation.

BACKGROUND: Global developmental delay (GDD) frequently precedes intellectual disability (ID), but no validated multivariable prognostic tool exists t...

A Risk Prediction Model for Normal-Tension Glaucoma Progression Integrating Genetic Markers and Corneal Biomechanics.

OBJECTIVE: Normal-tension glaucoma (NTG) is characterized by progressive optic nerve damage despite intraocular pressure remaining consistently within...

Reducing Racial Disparities in Stroke Thrombolysis Using Fairness-Aware Machine Learning.

Racial and ethnic disparities in the administration of intravenous thrombolysis (IVT) for acute ischemic stroke (AIS) remain persistent, raising conce...

Neuromorphic Optical Tracking and Imaging of Randomly Moving Targets Through Dynamic Dense Scattering Media.

Imaging and tracking objects moving along random, unpredictable trajectories through dense scattering media remains an open challenge with direct appl...

Deep Learning-guided Late-Phase Dynamic Frame Prediction for Accelerated Patlak Analysis in 18F-FDG-PET Brain Imaging.

PURPOSE: Dynamic 18F-FDG-PET enables the quantitative assessment of cerebral glucose metabolism but requires prolonged acquisition times, which pose c...

Deep Learning-Based Automated Segmentation of the Suprascapular Nerve and Adjacent Structures in Dynamic Supraclavicular Ultrasound.

OBJECTIVE: The suprascapular nerve (SSN) provides major motor and sensory innervation to the shoulder. Its accurate identification on ultrasound is ch...

Machine learning for functional outcome prediction after vestibular schwannoma surgery: a systematic review and diagnostic test accuracy meta-analysis.

PURPOSE: Machine learning (ML) models have been increasingly applied to predict postoperative facial nerve dysfunction and hearing preservation after ...

Personalized adaptive virtual reality experience driven by electroencephalography-based pain recognition.

BACKGROUND: Non-pharmacological pain management represents an urgent clinical need. Emerging technologies such as virtual reality (VR) and electroence...

3D U-net for volumetric segmentation of spinal cord MRI in degenerative disorders.

The correct identification of spinal cord structures in magnetic resonance imaging (MRI) plays a vital role in identifying degenerative spondyloarthro...

Advancing Non-Invasive Diagnosis through Biomarker Detection in Biofluids.

Early and accurate identification of disease-associated biomarkers is essential for timely diagnosis and the advancement of personalized healthcare. A...

Predicting Indwelling Catheter Duration in Spinal Cord Injury Patients With Neurogenic Bladder Using Machine Learning.

OBJECTIVE: To develop and validate interpretable machine learning (ML) survival models for predicting indwelling catheter time (ICT) in early-stage sp...

Machine Learning-Based Discrimination of Treatment-Resistant Schizophrenia Using Structural Brain Imaging: A Multi-Site Proof-of-Concept Study.

BACKGROUND: Treatment-resistant schizophrenia (TRS) affects 20-30% of individuals with schizophrenia, with persistent symptoms, functional impairment,...

Dynamic and Objective Quantification of Facial Nerve Palsy Using Smartphone-Based 3D Facial Geometry.

OBJECTIVE: To test whether smartphone-captured high-resolution 3D facial geometry can objectively quantify the severity of facial nerve palsy (FNP). M...

A Perovskite Memdiode-Based Neuromorphic in Silico Surrogate Model for Emulating Predictive Coding Failure in Diabetic Neuropathy.

Halide perovskite memdiodes have coupled ionic-electronic dynamics and are promising candidates for artificial synapses in neuromorphic computing. We ...