Clinical Trials

A functional system-informed graph neural network framework to quantify interpretable brain dysfunction in ASD.

Autism Spectrum Disorder (ASD) is one of the most common neurodevelopmental disorders affecting patients from childhood to adulthood. Yet, its pathological mechanism has not been conclusively established, and its diagnosis mainly depends on subjective assessments of clinical or behavioral measures. In the human brain, connectivity is ubiquitous between brain regions that share functional propertie...

LLM-based pedagogical agent for ICU simulation instructor training: A quasi-experimental study.

BACKGROUND: Intensive Care Unit (ICU) nursing is demanding, requiring advanced clinical decision-making and emergency management skills. Simulation-based instruction is central to ICU nursing education but remains constrained by the cost and time required for scenario authoring, limited faculty capacity for feedback, and slow content updates. Large language models (LLMs)-based pedagogical agents m...

Assessing the User Experience of an LLM-Based Conversational Assistant in Diabetes Mellitus Care.

UNLABELLED: This article presents the design, implementation, and evaluation of MarIA, a GPT-3.5-powered virtual assistant integrated into a messaging...

Malignant pleural mesothelioma classification and survival prediction with CT imaging using ResNet.

OBJECTIVES: This study aims to achieve accurate differentiation of malignant pleural mesothelioma (MPM) from metastatic pleural disease (MPD) and to p...

Impact of hypotension prediction index-guided management on intraoperative hypotension and postoperative outcomes in abdominal surgery: A meta-analysis of randomized controlled trials.

BACKGROUND: The Hypotension Prediction Index (HPI) is a machine-learning algorithm designed to predict hypotension. by maintaining mean arterial press...

Integrated multi-task learning framework for hepatocellular carcinoma segmentation and histological grading using fused multi-phase MRI.

OBJECTIVE: This study aims to develop and validate an integrated multi-task framework for hepatocellular carcinoma analysis by combining deep learning...

Using precision medicine methods to identify disease stages for chronic limb-threatening ischemia in participants of the BEST-CLI trial.

BACKGROUND: Chronic limb-threatening ischemia (CLTI), the most severe form of peripheral artery disease, is associated with a high risk of limb loss. ...

Hierarchical reinforcement learning with kill chain-informed multi-objective optimization to enhance resilience in autonomous unmanned swarm.

The resilience of autonomous unmanned swarms (AUS) serves as a cornerstone for guaranteeing continuous mission execution in the face of adversarial in...

Long-Term Survival and Beneficiaries of Adjuvant Anti-PD-1 Therapy in Resected Hepatocellular Carcinoma.

BACKGROUND: Early and late hepatocellular carcinoma (HCC) recurrences, which are driven by residual and de novo tumors, respectively, differ in biolog...

Deep learning-enhanced 3D imaging unveils semaglutide impact on cardiac fibrosis.

BACKGROUND AND PURPOSE: Myocardial fibrosis (MF), a hallmark of structural cardiac remodelling, drives disease progression across most forms of heart ...

Shallow and ensemble deep randomized neural network for anomaly detection.

Anomaly detection or one-class classification (OCC) plays a vital role in real-world applications. Traditional support vector machine-based OCC models...

A Window Into Vulnerability and Support: At-Risk Mothers' Perinatal Experiences and Perspectives on mHealth-Based Care.

AIM: To examine the perinatal experiences of at-risk mothers and their engagement with mobile-health-based care. DESIGN: A qualitative descriptive stu...

The role of AI in pre-analytical phase - use cases.

The pre-analytical phase of laboratory testing, encompassing processes from test ordering to sample analysis, represents the most error-prone componen...

Neurophysiological mechanisms and predictive modeling of SSRI treatment response in depression disorder based on multidimensional EEG features.

BACKGROUND: Depression exhibits significant heterogeneity in antidepressant treatment response. This study aimed to develop an Electroencephalography ...

A systematic review of the role of wearable devices and artificial intelligence applications in assessing functional outcomes after lumbar fusion.

BACKGROUND CONTEXT: As the population ages, rates of lumbar spine disease have risen, and lumbar fusion surgeries have become more prevalent. There ha...

The Pediatric Surgeon's AI Toolbox: How Large Language Models Like ChatGPT Are Simplifying Practice and Expanding Global Access.

INTRODUCTION: Pediatric surgeons face substantial administrative workload. Large language models (LLMs) may streamline documentation, family communica...

Artificial Intelligence 3D Augmented Reality-guided Robotic Prostatectomy Versus Cognitive MRI Intervention: Results of the Prospective Randomized RIDERS Trial.

BACKGROUND AND OBJECTIVE: Three-dimensional (3D) augmented reality (AR) and artificial intelligence (AI) technologies have recently been introduced to...

Randomized deep Hopfield network with multiple output layers for volatility time series forecasting.

Volatility forecasting plays a critical role in risk management and financial decision-making by facilitating the prediction of market fluctuations. H...

Comorbid anxiety predicts lower odds of MDD improvement in a trial of smartphone-delivered interventions.

Comorbid anxiety disorders are common among patients with major depressive disorder (MDD), but their impact on outcomes of digital and smartphone-deli...

Real-Time Artificial Intelligence-Assisted Middle Meningeal Artery Embolization Using Liquid Embolic Agents for Chronic Subdural Hematoma: A Preliminary Experience.

BACKGROUND AND OBJECTIVES: Middle meningeal artery (MMA) embolization is an emerging treatment option for chronic subdural hematoma. Surgeons must pay...