Emergency Medicine

Optimizing treatment strategies for femoral neck fractures using a machine learning model.

BACKGROUND: Recently, advances in machine learning models have allowed for automatic and highly accurate detection of fractures. To date, however, no machine learning models have been developed for the automatic selection of treatment strategies for femoral neck fractures. This study aimed to develop and evaluate the performance of a machine learning model that would recommend either internal fixa...

Vertebral fractures identified on lateral DXA images by deep learning predict incident fractures in older women.

UNLABELLED: XVFA is an AI-based method for identifying vertebral fractures on DXA images. In 423 women followed for 8 years, vertebral fractures identified by XVFA or manual assessment were associated with a twofold increased risk of incident fractures. XVFA predicted fracture risk comparably to manual assessment, supporting automated vertebral fracture detection. PURPOSE: Vertebral fractures (VFs...

Transfer learning for Multi-institutional Classification of Intussusception and Splenomegaly in Pediatric Abdominal Radiographs.

To address diagnostic delays in pediatric abdominal emergencies, this study aimed to develop and validate multi-institutional deep learning models for...

Molecular mechanism leading to human coronary atherosclerosis assessed by proteomic analysis and RNA sequences.

BACKGROUND AND AIMS: Atherosclerosis results from cellular and extracellular changes in the arterial wall, preceded by molecular shifts that initiate ...

Artificial Intelligence (Pattern Recognition) in Musculoskeletal Imaging: The Future or Hype?

Artificial intelligence and automated pattern recognition, in particular, have been described as the next frontier in musculoskeletal imaging. However...

Dissecting the pathobiology of suspected sepsis through a comparative analysis of endothelial inflammatory and clinical prediction models.

Sepsis remains a formidable challenge in critical care, and is characterized by profound circulatory and cellular abnormalities driven by both systemi...

Artificial intelligence for predicting hospital admissions from the emergency department: a prospective, quasi-experimental study.

The use of certain artificial intelligence (AI) tools may improve hospital operational efficiency, in particular in overcrowded emergency departments ...

Enhancing Anatomy Learning Through Enhanced 3D Visualization: A Study on Student Perception and Engagement in Museum-Based Education.

Effective anatomy education is essential for medical training. While traditional anatomy instruction provides essential foundational knowledge, its ef...

Machine learning-based prediction of diabetic retinopathy using clinlabomics: a multi-center study.

BACKGROUND: Diabetic retinopathy (DR) is a leading cause of vision loss, yet conventional retinal screening remains costly and resource-intensive. Thi...

A TyG-UHR-based machine learning model for screening lean MAFLD: development and external validation.

BACKGROUND: Lean metabolic dysfunction-associated fatty liver disease (MAFLD) is increasingly recognized but often goes unnoticed during health checku...

Identifying potential relationships between air pollutants and neutrophil extracellular traps formation in metabolic dysfunction-associated fatty liver disease by integrating computational toxicology and multi-omics data.

Metabolic dysfunction-associated fatty liver disease (MASLD) is a highly prevalent liver condition with a complex etiology increasingly linked to air ...

Prediction of Acute Liver Injury Trajectory in Patients Following Acetaminophen Overdose: A Multibiomarker Machine Learning Proof-of-Concept Study.

Clinical translation of novel therapies can be hindered by heterogeneity-driven sample size inflation in late-stage trials. In acetaminophen-induced l...

Molecular mechanisms linking cadmium chloride exposure to ankylosing spondylitis: an integrative network-based study.

Chronic cadmium chloride (CC) exposure is associated with diverse toxicological outcomes, yet its potential role in the pathogenesis of ankylosing spo...

Combining Machine Learning Models and Screening to Enhance Suicide Risk Identification for American Indian Patients: Retrospective Cohort Study.

BACKGROUND: American Indian and Alaska Native communities experience disproportionately high suicide rates. While machine learning (ML) models leverag...

State of the Art: Scapholunate Ligament Injuries.

ABSTRACT: Scapholunate ligament injuries are the most common ligamentous injuries of the wrist and typically result from high-energy trauma, most ofte...

Noninvasive neurophysiological diagnostics of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage: a scoping review.

BACKGROUND: Delayed cerebral ischemia (DCI) is a major complication following aneurysmal subarachnoid hemorrhage (aSAH), affecting outcomes. Given its...

GATA3 mediates the cardioprotective effect of hydroxysafflor yellow a against septic cardiomyopathy: insights from integrated machine learning, WGCNA, and experimental validation.

UNLABELLED: Sepsis-induced cardiomyopathy represents a life-threatening complication arising from severe sepsis and septic shock. Hydroxysafflor yello...

Can ChatGPT Take My Call? evaluating AI in gynecologic oncology telephone triage.

BACKGROUND: As access to artificial intelligence (AI) expands, patients and clinicians increasingly rely on these platforms for medical information an...

AI-based diagnostic evaluation of GPT-4o for crown-fracture detection on maxillary periapical radiographs: effects of prompt detail and customization.

BACKGROUND/AIM: Artificial intelligence (AI) and large language models (LLMs) are rapidly entering dental imaging workflows. We conducted a diagnostic...

Artificial intelligence for marine oil spill management: Recent advances and future directions.

Marine oil spills are one of the most severe anthropogenic threats to oceanic ecosystems, coastal communities, and global economic stability. While tr...