Emergency Medicine

AI-driven navigated hematoma aspiration vs. conservative treatment for spontaneous intracerebral hemorrhage: study protocol for a randomized controlled trial.

BACKGROUND: Spontaneous intracerebral hemorrhage (ICH) remains one of the most devastating types of stroke, with high mortality and long-term disability. Minimally invasive hematoma evacuation techniques have been developed to improve outcomes by reducing hematoma volume while minimizing surgical trauma compared with conventional craniotomy or conservative treatment. Artificial intelligence (AI) a...

Development and External Validation of a Deep Learning Model to Predict Mortality in Aneurysmal Subarachnoid Hemorrhage Using Admission Computed Tomography.

BACKGROUND AND OBJECTIVES: Traditional prognostication after aneurysmal subarachnoid hemorrhage depends on subjective clinical grading and radiological scoring systems that exhibit inter-rater variability and require multiple variables, hampering clinical adoption. We aimed to develop and externally validate a fully automated deep learning (DL) model predicting 90-day mortality exclusively from ad...

Variation in standards for reporting artificial intelligence research using burn image datasets raises ethical questions: A scoping review.

BACKGROUND: Accurate assessment of burn depth and area are required to guide treatment and inform prognosis. Currently this assessment relies on subje...

Current evidence and future directions for prophylactic anticoagulation therapy after severe spontaneous intracerebral hemorrhage: a narrative review.

Patients with spontaneous intracerebral hemorrhage (sICH) are at high risk for venous thromboembolism (VTE), a complication strongly associated with a...

Integration of AHRR methylation, heavy metals, and clinical characteristics for urothelial carcinoma risk stratification: An explainable artificial intelligence approach.

The short metabolic half-life of conventional tobacco biomarkers often limits their ability to reflect cumulative toxicological damage, which may comp...

Deep learning prediction of contrast extravasation versus intracranial hemorrhage after thrombectomy in patients with acute stroke.

AI Triage of Normal Chest Radiographs: A Silent Trial and Failure Analysis.

Chest radiography is the most frequently performed imaging examination worldwide, and increasing demand has contributed to reporting delays in many he...

Computational identification of diagnostic biomarkers linking Dibutyl phthalate exposure to ovarian cancer through network toxicology and machine learning.

Purchasing and Using an Artificial Intelligence Algorithm for Fracture Detection: Point-One of the Most Justifiable and Mature Applications in Radiology Today.

Purchasing and Using an Artificial Intelligence Algorithm for Fracture Detection: Counterpoint-Practical Barriers to Clinical Adoption.

Plaque-Level Machine Learning Prediction of Intraplaque Hemorrhage in Carotid Arteries Using Computed Tomography Angiography.

BACKGROUND: Carotid artery plaques, especially those with intraplaque hemorrhage (IPH), are significant contributors to ischemic stroke. Although high...

Machine Learning-Based Prediction of Critical Deterioration in the PICU.

BACKGROUND: Existing PICU early warning systems lack sufficient accuracy and timeliness for effective preparation. Machine learning approaches may imp...

FDA-authorized oncology artificial intelligence and machine learning devices and their clinical evidence: A cross-sectional analysis.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) tools are increasingly embedded in cancer care, yet the scope of U.S. Food and Drug...

Exploring the toxicological impact of perfluorooctanoic acid and perfluorooctane sulfonate on glioblastoma through network toxicology, machine learning, and multi-dimensional bioinformatics analysis.

Exposure to perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) has been associated with the development of various malignant tumors. H...

Development of Mind-Body Interventions for the Emergency Department.

Emergency department (ED) visits are a common experience that most individuals will have across their lifetime. ED-based evaluation for acute complain...

SuperLearner approach for predicting imminent risk of fracture in older Chinese patients with newly diagnosed osteoporosis based on their routine blood test markers.

Advancing nanomedicine with machine learning: predicting protein corona and nano-bio interactions.

The protein corona (PC) has widely been demonstrated to impact the pharmacokinetics, safety, and efficiency of nanoparticle (NP) systems for clinical ...

The Hospital Organ Donation Performance Score: A human and artificial intelligence/machine learning collaborative solution to the current nationwide puzzling state of organ donation metrics.

BACKGROUND: Efforts to ameliorate the current organ shortage have predominantly focused on improving processes among the nation's organ procurement or...

From prediction to practice: early implementation of a machine learning-based hospitalization prediction tool in the emergency department.

BACKGROUND: Machine learning (ML) models can accurately predict hospital admissions in emergency departments (EDs), but real-world adoption remains ra...

Artificial Intelligence-assisted Periapical Radiographic Assessment: Lesion Detection, Endodontic Complication Analysis, and Review of Clinical Treatment Recommendations.

INTRODUCTION: Artificial intelligence (AI) systems are increasingly used in dental radiology to support endodontic diagnosis. However, their diagnosti...