Strokes

From Continuous Predictors to Clinical Thresholds: Early Evidence on Performance Trade-offs of Guideline-Based Categorisation for Ischaemic Stroke Outcome Prediction

Machine learning models achieve strong predictive accuracy for 90-day outcome prediction in acute ischaemic stroke, yet clinical adoption is limited by the misalignment of model explanations with clinicians' reasoning. Motivated by a clinician user study calling for clinical guideline-aligned cut-offs, we ask whether continuous predictors can be replaced by clinically informed categorical encoding...

Diagnostic Accuracy of a locally deployed Large Language Model Algorithm for Automated Code Stroke Pathway Identification in Emergency Department Triage Notes

Background: Delayed Code Stroke activation contributes to worse outcomes in acute stroke. Emergency Department (ED) triage notes contain free-text clinical information that could enable automated, real-time pathway activation. We evaluated the diagnostic accuracy of a multi-pass large language model (LLM) pipeline for identifying patients meeting Code Stroke criteria from ED triage notes. Methods:...

Bridging Online and Offline Handwriting via Differentiable Physical Rendering

Realistic handwritten text generation plays an important role in numerous applications, such as font design, biometric authentication, and robotic cal...

FOUND-AF: Benchmarking ECG Foundation Models for Atrial Fibrillation Detection

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with increased risks of stroke, heart failure, and mortalit...

DiagLoop: A Counterfactual Data Flywheel with Stage-Localized Reinforcement for Diagnostic LLMs

Causal diagnostic models must explain how conclusions follow from evidence because diagnoses guide repairs and treatments. Yet serious cases are scarc...

Machine learning to detect intraoperative ischemia from electroencephalography in carotid endarterectomy surgery

Cerebral ischemia is a significant concern during high-risk surgeries, such as carotid endarterectomy (CEA). Continuous electroencephalography, monito...

Hybrid novice-AI system achieves expert-level performance in intraoperative ischemia detection

Carotid endarterectomy carries the risk of intraoperative cerebral ischemia, which is monitored by expert neurophysiologists through continuous electr...

On the Efficacy of Self-Supervised Point Cloud Encoders for Efficient 3D Large Language Models

3D point cloud-language models (3D-LLMs) enable 3D understanding by pairing point cloud encoders with large language models, but existing methods rely...

Machine learning-based neuroimaging for prediction of deep brain stimulation outcomes in movement disorders: Systematic review and meta-analysis

Abstract Background: Deep Brain Stimulation (DBS) surgery is a treatment of choice for movement disorders, and utilizes an implanted electrical pulse ...

Precision Management of Fludrocortisone-Related Hypertension Risk in Congenital Adrenal Hyperplasia: A Machine Learning Approach to Personalized Dosing

Congenital adrenal hyperplasia (CAH) is a rare inherited disorder requiring lifelong hormone replacement therapy. Excessive hormone replacement poses ...

FSB-Net: Frequency-Spatial Boundary Network for Brain Stroke Lesion Segmentation in Non-Contrast CT

Accurate segmentation of brain stroke lesions in non-contrast computed tomography (NCCT) scans is critical for rapid clinical decision-making, yet rem...

CardioMeta: Calibrated Multi-Task Prediction of Diabetes, Hypertension, and Cardiovascular Disease Across Population and EHR Data

Cardiometabolic diseases remain among the most persistent drivers of preventable morbidity because diabetes, hypertension, and cardiovascular disease ...

Machine learning and data-driven models for predicting post-stroke dysphagia: a systematic review and meta-analysis

Background: Post-stroke dysphagia (PSD) contributes to aspiration, pneumonia, malnutrition, prolonged hospitalization and mortality. We evaluated the ...

Carotid Plaque Calcification Associates with Cardiac Morbidity, Mortality and Systemic Atherosclerotic Disease in a Sex-Specific Manner

Background Carotid plaque calcification is commonly interpreted as a marker of atherosclerotic burden, but its prognostic meaning may depend on calcif...

NVAITC AI Scientist: A Governed End-to-End Research System -- A Hypertension GWAS Case Study

Agentic research systems are emerging as a new paradigm for coordinating scientific workflows beyond isolated model inference, code generation, or sta...

Lesioned Multimodal Language Models Reproduce Aphasic Picture-Naming Patterns

Aphasia following stroke commonly produces systematic naming errors with characteristic profiles, but whether general-purpose language models not desi...

FairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness

Algorithmic fairness methods are increasingly used to identify and mitigate bias in machine learning models, yet most approaches are evaluated in isol...

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...

Effect of initiating an ARB- versus ACEI-based regimen on dementia risk, a target trial emulation of 2.5 million US Veterans

Background: Hypertension is a modifiable risk factor for dementia, yet the comparative effectiveness of angiotensin receptor blockers (ARBs) versus an...

CerebAI: Explainable Three-Class Stroke CT Classification via ConvNeXt and Integrated Gradients

Stroke is a leading cause of death and long-term disability worldwide, affecting approximately 15 million individuals annually. Prompt and accurate su...