Neurology

Explainable Longitudinal Machine Learning for Dementia Progression Using Cognitive and MRI Biomarkers

Dementia is a progressive neurological condition characterized by cognitive decline and structural brain changes that evolve. Longitudinal modeling of these changes is important for improving disease monitoring, identifying progression patterns, and supporting early risk stratification. This study developed an explainable longitudinal machine-learning framework for dementia progression, using cogn...

Lost in Visual Translation: A VLM-Assisted Perceptual-Semantic Coherence Framework for EEG-to-Image Reconstruction

EEG-to-image evaluation should distinguish visual fidelity from recoverable meaning. Yet EEG-derived reconstructions are blurry, distorted, and low-detail, causing SSIM, LPIPS, and CLIP to penalize semantically recoverable outputs or reward plausible but incorrect ones. We analyze 6,855 ground-truth/reconstruction pairs from ATM, ENIGMA, BrainVis, and DreamDiffusion using semantic probes, caption ...

Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts

Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of rea...

Explainable Machine Learning Models for Alzheimer's Diagnosis Using Routine and Low-Cost Clinical Data

Emerging as a significant global health challenge, Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that causes memory loss and co...

Aqueous Humor Liquid Biopsy Enables Multi-Omics Tumor Profiling and Methylation-Based Machine-Learning Stratification of Retinoblastoma

Primary tumor biopsy in retinoblastoma carries an unacceptable risk of extraocular dissemination. As a result, children treated with eye-sparing appro...

LoSA-Net: A Localized and Scale-Adaptive Network for Boundary-Sensitive Prediction of Perineural Invasion in 3D MRI

Perineural invasion (PNI) is a clinically relevant indicator of tumor aggressiveness and can influence surgical decision-making, motivating interest i...

DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations

Deep learning for EEG-based seizure detection faces critical challenges: severe annotation scarcity and extreme class imbalance, where ictal events co...

Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts

Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of rea...

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning

Quantitative Structure-Activity Relationship ($\mathtt{QSAR}$) modeling is a foundational computational methodology in early-stage drug discovery, hea...

Integrating Physics-Informed Neural Networks and 3D Vascular Geometry Learning for Cerebral Aneurysm Detection and Multimodal Rupture-Risk Prediction

Cerebral aneurysms are localized dilations of intracranial arteries that may rupture and cause subarachnoid hemorrhage. Current assessment relies on h...

Projection-Domain Sensitivity Analysis of Vertebral DRRs Under Intrinsic Calibration Perturbation

Accurate geometric calibration is essential for fluoroscopy-guided spinal imaging, digitally reconstructed radiograph (DRR) generation, and 2D--3D ver...

Assessing AI and Neurologist Diagnostic Reasoning Against Neuropathological Ground Truth

BACKGROUND Accurate differential diagnosis of complex neurological disorders remains challenging due to overlapping clinical features and heterogeneou...

Automated Interpretation of EEG Reports Using a Large Language Model with Structured Confidence Outputs

Background: Free-text EEG reports typically lack structure, hindering scalable analysis. We evaluate a large language model (LLM) pipeline to extract ...

Neonatal Seizure Detection Using Combined aEEG and Compressed Spectral Array Features: A Machine-Learning Proof-of-Concept Study

Background To build a clinically translatable neonatal seizure detection algorithm using amplitude-integrated electroencephalography (aEEG) and compre...

Early Prediction of Parkinson's Disease Progression by Integrating Research Cohort and Real-World Data Using Knowledge-Anchored Graph Learning

Parkinson disease (PD) progression is highly heterogeneous. Deeply phenotyped longitudinal research cohorts have enabled characterization of PD progre...

GaitEncoder: A Foundation Model of Gait Kinematics for Diverse Clinical Applications and Pathologies

Quantitative gait analysis could enhance personalized treatment for many movement-related conditions; however, it is not routinely integrated into cli...

Explainable machine learning for the prediction of motor fluctuations and Levodopa-induced dyskinesias in Parkinson's disease

Background: Motor complications, such as motor fluctuations and Levodopa-induced dyskinesias (LID), significantly impair quality of life in persons wi...

Uncertainty-aware extraction of clinical findings from Finnish EHRs using open large language models

Objective. To evaluate whether open-weight large language models (LLMs) can accurately extract clinical findings from Finnish-language pediatric recor...

DATA-DRIVEN IDENTIFICATION OF SEX DIFFERENCES IN CEREBRAL BLOOD FLOW USING ARTERIAL SPIN LABELLING AND EXPLAINABLE ARTIFICIAL INTELLIGENCE

Purpose: To investigate sex differences in cerebral blood flow through densely parcellated cortical and subcortical regions using explainable artifici...

BCCWJ-Brain: A Multi-Modal fMRI, MEG, and EEG Dataset of Naturalistic Japanese Reading

We present the BCCWJ-Brain dataset, a multi-modal neuroimaging resource comprising functional magnetic resonance imaging (fMRI), magnetoencephalograph...