Strokes

AI-driven diagnosis of vulnerable intracranial atherosclerotic plaques using large language models and vision transformers: a multi-center study.

OBJECTIVES: High-resolution vessel wall imaging (HR-VWI) is essential for diagnosing vulnerable intracranial atherosclerotic plaques, but its interpretation requires expertise. This study investigates the integration of large language models (LLMs) and deep learning (DL) for more efficient diagnosis. MATERIALS AND METHODS: A retrospective study of symptomatic intracranial atherosclerotic stenosis ...

Distributed Precision Stroke Care: Artificial Intelligence-Driven Stroke Management Using Multimodal Sensor Data.

Delays in stroke diagnosis contribute to long-term disability. Many patients still face barriers to effective risk factor management, timely detection, and access to poststroke rehabilitation. The emergence of artificial intelligence-enabled, consumer-facing health technologies offers a transformative opportunity to address these gaps across the stroke care continuum. This review examines the evol...

Blood-based Vienna 3P/5P risk models accurately predict first hepatic decompensation in compensated advanced chronic liver disease.

BACKGROUND & AIMS: Invasive measurement of hepatic venous pressure gradient (HVPG) is the gold standard for diagnosing clinically significant portal h...

GPT-4o in Nutrition for Inpatients Undergoing Post-Stroke Rehabilitation: Identifying Dietary Errors, Exploring Expert-AI Rationale Differences, and Structuring AI-Expert Collaboration.

OBJECTIVE: Inpatients undergoing stroke rehabilitation experience high malnutrition rates, requiring strict dietary management. However, manual and ti...

Robotic rehabilitation and intelligent algorithms improving the performance skills of stroke patients: a scoping review.

BACKGROUND: This scoping review highlights major advances and persisting gaps in robotic and AI-driven rehabilitation for stroke, evaluating their imp...

Precision Nutritional Genomics, Gut Microbiota and Artificial Intelligence in Chronic Kidney Disease.

Chronic kidney disease (CKD) is a prevalent global health issue, and nutritional management of CKD is an integral component through all stages of the ...

Connectome-based markers predict the sub-types of frontotemporal dementia.

Frontotemporal dementia (FTD) presents a complex spectrum of neurodegenerative disorders, encompassing distinct subtypes with varied clinical manifest...

Artificial Intelligence-Based Algorithms Improve Care of Patients with AAA.

BACKGROUND: Timely detection and monitoring of abdominal aortic aneurysms (AAAs) are necessary to prevent ruptures and decrease mortality. Artificial ...

Comparative effects of dynamic and static stretching on spasticity reduction in stroke patients using a robotic hand-wrist stretching device.

BACKGROUND: Spasticity is a common consequence of upper motor neuron syndrome, affecting approximately 42.6% of stroke patients and impairing quality ...

Automated detection of neonatal pulmonary hypertension in echocardiograms with a deep learning model.

BACKGROUND: In infants, pulmonary hypertension (PH) increases morbidity and mortality. Echocardiography, though standard, is time- and expertise-deman...

Development of an interpretable machine learning model to predict short-term bleeding risk in patients receiving dual antithrombotic therapy following cardiac surgery.

INTRODUCTION: Bleeding is a serious complication in cardiac surgery, especially among patients receiving combined anticoagulant and antiplatelet thera...

Exploiting Cross-modal Collaboration and Discrepancy for Semi-supervised Ischemic Stroke Lesion Segmentation from Multi-sequence MRI Images.

Accurate ischemic stroke lesion segmentation is useful to define the optimal reperfusion treatment and unveil the stroke etiology. Despite the importa...

Imaging in chronic thromboembolic pulmonary hypertension: review of the current literature.

Chronic thromboembolic pulmonary hypertension (CTEPH) is a severe, life-threatening complication of pulmonary embolism with pulmonary hypertension (PH...

Perspectives of Occupational Therapists Toward Robot-Assisted Therapy for Stroke Survivors: A Scoping Review.

Robot-assisted therapy, such as exoskeletons and soft robotics gloves, shows promise for stroke rehabilitation by improving upper limb function, altho...

Mask-Guided and Fidelity-Constrained Deep Learning Model for Accurate Translation of Brain CT Images to Diffusion MRI Images in Acute Stroke Patients.

The early and precise diagnosis of stroke plays an important role in its treatment planning. Computed Tomography (CT) is utilised as a first diagnosti...

Unsupervised machine learning to explore inflammation following cardiopulmonary bypass.

IntroductionCardiac surgery with cardiopulmonary bypass (CPB) often induces systemic inflammatory reaction syndrome (SIRS), affecting postoperative ou...

Robust Quantification of Affected Brain Volume from Computed Tomography Perfusion: A Hybrid Approach Combining Deep Learning and Singular Value Decomposition.

Volumetric estimation of affected brain volumes using computed tomography perfusion (CTP) is crucial in the management of acute ischemic stroke (AIS) ...

Evaluating the diagnostic accuracy of AI in ischemic and hemorrhagic stroke: A comprehensive meta-analysis.

Stroke poses a significant health challenge, with ischemic and hemorrhagic subtypes requiring timely and accurate diagnosis for effective management. ...

Machine Learning in Venous Thromboembolism-Why and What Next?

Venous thromboembolism (VTE) remains a leading cause of cardiovascular morbidity and mortality, despite advances in imaging and anticoagulation. VTE a...

An Adaptive Multi-Stage and Adjacent-Level Feature Integration Network for Brain Tumor Image Segmentation.

The segmentation of brain tumor magnetic resonance imaging (MRI) plays a crucial role in assisting diagnosis, treatment planning, and disease progress...