Strokes

Automatic quantification of left atrium volume for cardiac rhythm analysis leveraging 3D residual UNet for time-varying segmentation of ECG-gated CT.

Atrial fibrillation (AF) is a heart condition widely recognized as a significant risk factor for stroke. Left atrial (LA) volume variation has been identified as a key predictor of AF, and several researchers have proposed deep learning models capable of quickly providing this measurement by processing computed tomography (CT) or magnetic resonance images. In clinical imaging, time-varying ECG-gat...

Automatic collateral quantification in acute ischemic stroke using U-net.

OBJECTIVES: To harness the U-Net deep learning framework for automated quantification of collateral circulation in acute ischemic stroke (AIS) via computed tomography angiography (CTA) images, comparing its performance against traditional visual collateral scores (vCS).

Association of early enoxaparin prophylactic anticoagulation with ICU mortality in critically ill patients with chronic obstructive pulmonary disease: a machine learning-based retrospective cohort study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major contributor to global morbidity and mortality, particularly during acute exacerbat...

Emerging biomaterials and bio-nano interfaces in pulmonary hypertension therapy: transformative strategies for personalized treatment.

Pulmonary hypertension (PH) is still an aggressive and progressive illness with vascular remodeling and right heart failure despite the therapeutic ad...

Cyber security Enhancements with reinforcement learning: A zero-day vulnerabilityu identification perspective.

A zero-day vulnerability is a critical security weakness of software or hardware that has not yet been found and, for that reason, neither the vendor ...

Predictive Value of Machine Learning Models for Cerebral Edema Risk in Stroke Patients: A Meta-Analysis.

INTRODUCTION: Stroke patients are at high risk of developing cerebral edema, which can have severe consequences. However, there are currently few effe...

Application of machine learning algorithms in predicting new onset hypertension: a study based on the China Health and Nutrition Survey.

BACKGROUND: Hypertension is a serious chronic disease that can significantly lead to various cardiovascular diseases, affecting vital organs such as t...

Artificial Intelligence in Transcranial Doppler Ultrasonography.

Transcranial Doppler is an instrumental ultrasound method capable of providing data on various brain pathologies, in particular, the study of cerebral...

StackAHTPs: An explainable antihypertensive peptides identifier based on heterogeneous features and stacked learning approach.

Hypertension, often known as high blood pressure, is a major concern to millions of individuals globally. Recent studies have demonstrated the signifi...

Optimizing Stroke Detection Using Evidential Networks and Uncertainty-Based Refinement.

Evaluating neurological impairments post-stroke is essential for assessing treatment efficacy and managing subsequent disabilities. Conventional clini...

Stroke Prediction using Clinical and Social Features in Machine Learning

Every year in the United States, 800,000 individuals suffer a stroke - one person every 40 seconds, with a death occurring every four minutes. While...

Faces Speak Louder Than Words: Emotions Versus Textual Sentiment in the 2024 USA Presidential Election

Sentiment analysis of textual content has become a well-established solution for analyzing social media data. However, with the rise of images and v...

Generating Completions for Fragmented Broca's Aphasic Sentences Using Large Language Models

Broca's aphasia is a type of aphasia characterized by non-fluent, effortful and fragmented speech production with relatively good comprehension. Sin...

A Comparative Study on Machine Learning Models to Classify Diseases Based on Patient Behaviour and Habits

In recent years, ML algorithms have been shown to be useful for predicting diseases based on health data and posed a potential application area for ...

Learning from Impairment: Leveraging Insights from Clinical Linguistics in Language Modelling Research

This position paper investigates the potential of integrating insights from language impairment research and its clinical treatment to develop human...

Graph convolutional networks enable fast hemorrhagic stroke monitoring with electrical impedance tomography

Objective: To develop a fast image reconstruction method for stroke monitoring with electrical impedance tomography with image quality comparable to...

Reconstructing Quantitative Cerebral Perfusion Images Directly From Measured Sinogram Data Acquired Using C-arm Cone-Beam CT

To shorten the door-to-puncture time for better treating patients with acute ischemic stroke, it is highly desired to obtain quantitative cerebral p...

Automatic Prediction of Stroke Treatment Outcomes: Latest Advances and Perspectives

Stroke is a major global health problem that causes mortality and morbidity. Predicting the outcomes of stroke intervention can facilitate clinical ...

Domain-Agnostic Stroke Lesion Segmentation Using Physics-Constrained Synthetic Data

Segmenting stroke lesions in Magnetic Resonance Imaging (MRI) is challenging due to diverse clinical imaging domains, with existing models strugglin...

SNOOPI: Supercharged One-step Diffusion Distillation with Proper Guidance

Recent approaches have yielded promising results in distilling multi-step text-to-image diffusion models into one-step ones. The state-of-the-art ef...