Neurology

Machine Diagnostics and Machine Phenotyping of Migraine: A HUNT Study.

BACKGROUND AND OBJECTIVES: In the absence of biomarkers, the true biological footprint of migraine r...

Is Migraine a Single Entity?: Machine Learning Reveals Underlying Heterogeneity.

EDRNet-based adolescent idiopathic scoliosis screening method using bare-back images.

BACKGROUND: Adolescent idiopathic scoliosis (AIS) affects 2-3% of adolescents. Current screening rel...

DAG-VAERL: a novel causal inference method for building causal gene regulatory networks.

BACKGROUND: Causal discovery methods provide a powerful tool for uncovering the causal relationships...

Proteomic and machine learning analysis predicts treatment response signatures in Myasthenia Gravis.

BACKGROUND: Myasthenia gravis (MG) is a prototypical antibody-mediated autoimmune disease with varia...

Prediction of Alzheimer's disease risk factors from retinal images via deep learning: Development and validation of biologically relevant morphological associations in the UK Biobank.

BackgroundThe systemic, metabolic, lifestyle factors have established associations with Alzheimer's ...

Longitudinal patient-reported outcomes after spinal fusion for adolescent idiopathic scoliosis: recovery trajectories and insights into surgical timing.

PURPOSE: Health-related quality-of-life (HRQOL) assessment provides insight into patient's subjectiv...

Neuroimaging studies of non-suicidal self-injury in young adults with major depressive disorder.

Non-suicidal self-injury (NSSI) is highly prevalent in adolescents with major depressive disorder (M...

Explainable EEG-based machine learning for early diagnosis of Alzheimer's disease and frontotemporal dementia.

The quick and accurate diagnosis of Alzheimer's disease (AD) and Frontotemporal Dementia (FTD) is a ...

Scalable CT-based prognostic modeling of dementia conversion in mild cognitive impairment.

UNLABELLED: Mild cognitive impairment (MCI) is a heterogeneous condition, with up to 40% of patients...

Towards a rational ECT treatment approach for severe and treatment resistant schizophrenia using neuroimaging biomarkers: A focused review.

Treatment resistant schizophrenia (TRS) is a major challenge in psychiatry, and its management remai...

Large language models integrated into brain-computer interfaces for communication and control: a systematic review.

Large language models (LLMs) are starting to be coupled with brain-computer interfaces (BCIs) for as...

A comprehensive review of biomechanical factors influencing pedicle screw fixation strength and the critical role of artificial intelligence.

BACKGROUND: Pedicle screws are pivotal in spinal fixation procedures, providing stability and facili...

Investigating AD and non-AD [18F]flortaucipir distribution patterns in a memory clinic cohort.

PURPOSE: Amyloid (A) deposition represents a specific pathological hallmark of Alzheimer's disease (...

A Multiscale Decoding Approach of Subject-Independent Motor Imagery EEG Signal Combined with Data Alignment Strategy.

PURPOSE: Brain-computer interface (BCI) leverages artificial intelligence (AI) and wearable electroe...

Diagnosis and Prediction of Alzheimer's Disease via a High-Level Convolutional Block Attention Module-Residual Network.

With the rapid development of artificial intelligence and medical image analysis, MRI-based automate...

Acoustic Analysis of Primary Care Patient-Clinician Conversations to Screen for Cognitive Impairment.

IMPORTANCE: Cognitive impairment (CI) is often underdetected in primary care due to time and resourc...

From Ambient Assistance to Data-Driven Precision in AI-Enabled Alzheimer's Care (2004-2025): A Bibliometric Perspective.

BACKGROUND AND OBJECTIVES: Population aging and the rising prevalence of Alzheimer's Disease and Rel...

Variational autoencoder for interpretable seizure onset phases detection.

OBJECTIVE: In this study, we describe a deep learning framework for automated seizure annotation in ...

From prediction to mechanism: Explainable AI uncovers plasma and CSF proteomic signatures of Alzheimer's disease.

Alzheimer's disease (AD) plasma and cerebrospinal fluid (CSF) proteomics can distinguish AD from cog...

A novel neural network model with SAGPooling graph decodes changes in cognitive trajectories.

Accurately predicting the progression of neurodegenerative diseases and identifying key brain region...