Neurology

Applicability of machine learning technique in the screening of patients with mild traumatic brain injury.

Even though the demand of head computed tomography (CT) in patients with mild traumatic brain injury (TBI) has progressively increased worldwide, only a small number of individuals have intracranial lesions that require neurosurgical intervention. As such, this study aims to evaluate the applicability of a machine learning (ML) technique in the screening of patients with mild TBI in the Regional U...

Identifying multilevel predictors of trajectories of psychopathology and resilience among juvenile offenders: A machine learning approach.

Mental ill health is more common among juvenile offenders relative to adolescents in general. Little is known about individual differences in their long-term psychological adaptation and its predictors from multiple aspects of their life. This study aims to identify heterogeneous trajectories of probable psychiatric conditions and their predictors. Participants included 574 juvenile offenders who ...

Artificial intelligence for dementia genetics and omics.

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can...

Individual-Level Prediction of Exposure Therapy Outcome Using Structural and Functional MRI Data in Spider Phobia: A Machine-Learning Study.

Machine-learning prediction studies have shown potential to inform treatment stratification, but recent efforts to predict psychotherapy outcomes with...

Machine learning with multimodal neuroimaging data to classify stages of Alzheimer's disease: a systematic review and meta-analysis.

In recent years, Alzheimer's disease (AD) has been a serious threat to human health. Researchers and clinicians alike encounter a significant obstacle...

Synthetic EMG Based on Adversarial Style Transfer Can Effectively Attack Biometric-Based Personal Identification Models.

Biometric-based personal identification models are generally considered to be accurate and secure because biological signals are too complex and perso...

Artificial intelligence for dementia drug discovery and trials optimization.

Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogene...

Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images.

Magnetic resonance imaging (MRI) is widely used for ischemic stroke lesion detection in mice. A challenge is that lesion segmentation often relies on ...

Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke.

Swift diagnosis and treatment play a decisive role in the clinical outcome of patients with acute ischemic stroke (AIS), and computer-aided diagnosis ...

Deep-Learning-Based Model for the Prediction of Cancer-Specific Survival in Patients with Spinal Chordoma.

OBJECTIVE: Spinal chordomas are locally aggressive and frequently recurrent tumors with a poor prognosis. Previous studies focused on a Cox regression...

Impairments of the ipsilesional upper-extremity in the first 6-months post-stroke.

BACKGROUND: Ipsilesional motor impairments of the arm are common after stroke. Previous studies have suggested that severity of contralesional arm imp...

Value Proposition of FDA-Approved Artificial Intelligence Algorithms for Neuroimaging.

PURPOSE: The number of FDA-cleared artificial intelligence (AI) algorithms for neuroimaging has grown in the past decade. The adoption of these algori...

Evaluation of the effectiveness of artificial intelligence for ultrasound guided peripheral nerve and plane blocks in recognizing anatomical structures.

BACKGROUND: We aimed to assess the accuracy of artificial intelligence (AI) based real-time anatomy identification for ultrasound-guided peripheral ne...

Integration of feedforward and feedback control in the neuromechanics of vertebrate locomotion: a review of experimental, simulation and robotic studies.

Animal locomotion is the result of complex and multi-layered interactions between the nervous system, the musculo-skeletal system and the environment....

MISPEL: A supervised deep learning harmonization method for multi-scanner neuroimaging data.

Large-scale data obtained from aggregation of already collected multi-site neuroimaging datasets has brought benefits such as higher statistical power...

Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy.

OBJECTIVE: This study aimed to explore sensitive detection methods for pathological high-frequency oscillations (HFOs) to improve seizure outcomes in ...

Using deep learning for an automatic detection and classification of the vascular bifurcations along the Circle of Willis.

Most of the intracranial aneurysms (ICA) occur on a specific portion of the cerebral vascular tree named the Circle of Willis (CoW). More particularly...

Evaluating the performance of large language models: ChatGPT and Google Bard in generating differential diagnoses in clinicopathological conferences of neurodegenerative disorders.

This study explores the utility of the large language models (LLMs), specifically ChatGPT and Google Bard, in predicting neuropathologic diagnoses fro...

Decoding movement kinematics from EEG using an interpretable convolutional neural network.

Continuous decoding of hand kinematics has been recently explored for the intuitive control of electroencephalography (EEG)-based Brain-Computer Inter...

A novel method for modeling effective connections between brain regions based on EEG signals and graph neural networks for motor imagery detection.

Classified as biomedical signal processing, cerebral signal processing plays a key role in human-computer interaction (HCI) and medical diagnosis. The...