Emergency Medicine

Development and validation of an automated MRI-based pipeline for temporal classification of intracerebral hemorrhage using U-Net segmentation and machine learning.

This study aimed to develop and validate an automated magnetic resonance imaging (MRI)-based pipeline for temporal classification of intracerebral hemorrhage (ICH) using clinically feasible multi-sequence MRI, 2.5D U-Net segmentation, and machine learning. This retrospective study included a development cohort of 56 patients and an independent validation cohort of 115 patients with ICH. Five MRI s...

Development and Internal Evaluation of a Deep Learning Model for Fine-Grained Detection of Dental Trauma on CBCT-Dominant Image Records.

OBJECTIVES: To develop and internally evaluate a YOLO-based model for fine-grained localization and classification of dental trauma subtypes. METHODS: This retrospective single-center study used a CBCT-dominant dataset of 1,256 annotated trauma instances (1,065 instances derived from sagittal CBCT images and 191 instances derived from periapical radiographs) to develop and internally evaluate a YO...

Autonomous Vascular Access Devices: Current Solutions and Future Challenges.

INTRODUCTION: Central venous cannulation is essential for life-saving interventions including resuscitation of critically ill patients, hemodynamic mo...

Small Intracerebral Hemorrhage: Baseline CT Edema-to-Hematoma Ratio as an Independent Predictor of Early Expansion.

BACKGROUND AND PURPOSE: Small intracerebral hemorrhage (ICH), defined as baseline NCCT hematoma volume (HV) <30 mL, is often considered lower risk for...

Patient Stratification for Improving Acute Chest Pain Management and Mitigating Emergency Department Crowding: Machine Learning Model Development and Validation.

BACKGROUND: Acute chest pain (ACP) is one of the most common chief complaints in the emergency department (ED), accounting for approximately 8% of all...

Systematic assessment of the medical utility of radiology and diagnostic Artificial Intelligence in fracture detection (SAMURAI-fracture): a protocol for a multicentre cluster-randomised controlled trial.

INTRODUCTION: Fracture misdiagnosis is a common diagnostic error in emergency departments (EDs) and minor injury units (MIUs), leading to poor patient...

A multi-platform comparison and machine learning associative analysis study on the information dissemination effect of short videos for cerebral hemorrhage: A cross-sectional study based on TikTok and Bilibili.

Cerebral hemorrhage is a critical public health issue marked by high incidence, disability, and mortality. In China, short-video platforms have become...

AI-assisted radiographic fracture detection and length of stay in the adult ambulatory orthopedic emergency department: a before-after cohort study with a disease-specific internal control.

BACKGROUND: Although artificial intelligence-assisted radiographic fracture detection tools (AI-RFDT) have demonstrated high diagnostic accuracy in ad...

Systematic Review on Noncomputed Tomography-Based Technologies for Prehospital Stroke Diagnosis.

Effects of stroke therapies area highly time dependent but onset-to-treatment times for recanalizing treatment are mostly beyond optimal time windows....

Large Language Models in Spine Surgery: A Scoping Review of Clinical Efficacy, Technical Integration, and Ethical Paradigms.

Study DesignScoping review.ObjectivesTo map spine literature on large language models, characterize reported use cases, and identify evidence gaps lim...

Clinical Laboratory Terminology Standardization for Semantic Interoperability Using a Large Language Model-Based Agent: Methodological Study.

BACKGROUND: Semantic interoperability, the ability of disparate health information systems to exchange and consistently interpret clinical data, is a ...

A grade-specific clinical prediction rule for independent walking after traumatic spinal cord injury.

OBJECTIVE: To identify significant predictors for individual American Spinal Injury Association Impairment Scale (AIS) grades, and develop a clinical ...

Triage-Time Machine Learning for Computed Tomography Acquisition Prioritization in the Emergency Department.

RATIONALE AND OBJECTIVES: To evaluate whether triage-time machine learning (ML) can improve computed tomography (CT) acquisition prioritization beyond...

3D Slicer-based preprocedural trajectory planning for transvenous mediastinal biopsy using automated segmentation: a technical note.

Histological diagnosis of mediastinal tumors can be challenging when safe puncture access is limited by adjacent airway and vascular structures. This ...

Patient-Facing AI System for Symptom Guidance Using Simulated Encounters and Physician Review: Analytical Validation Study.

BACKGROUND: Rapid advances in large language models (LLMs) have expanded interest in patient-facing health applications that support symptom assessmen...

Contemporary practice patterns of shock wave lithotripsy in the era of endourology: an international FUTURE survey.

The relative use of shock wave lithotripsy (SWL) has declined with the expansion of endourological techniques, although SWL remains widely available. ...

Computer vision-aided classification of insecticide-induced behavioral patterns in Aphis gossypii (Hemiptera: Aphididae).

Electrophysiological approaches are widely used to characterize insecticide target sites, whereas behavioral symptoms associated with different modes ...

Development and validation of a comprehensive prognostic model for 28-day ICU mortality in non-traumatic subarachnoid hemorrhage: an analysis based on the MIMIC-IV database.

BACKGROUND: Due to the complex pathophysiology of non-traumatic subarachnoid hemorrhage (SAH), accurate risk prediction remains a challenge. Our aim i...

Linking bisphenol a exposure to pancreatic cancer: Evidence from population epidemiology, multi-omics screening, and cellular validation.

Bisphenol A (BPA) is a ubiquitous environmental endocrine disruptor; its association with pancreatic cancer and its potential mechanisms of action rem...

MACHINE LEARNING VERSUS PENALISED LOGISTIC REGRESSION FOR PREDICTING IN-HOSPITAL MORTALITY IN RUPTURED ABDOMINAL AORTIC ANEURYSM: A COMPARISON USING A UNIFORM VALIDATION FRAMEWORK.

BACKGROUND: Ruptured abdominal aortic aneurysm (rAAA) remains associated with substantial in-hospital mortality. Although machine-learning methods can...