Emergency Medicine

MultiDentNet: a unified deep learning framework for multi-class dental condition screening and preliminary oral lesion triage.

To develop and evaluate MultiDentNet, a unified deep learning framework for multi-class dental condition screening and preliminary risk stratification of cancer-suspicious oral lesions, utilizing backbone-diverse ensembling and inter-class relational modeling. Four complementary CNN backbones (DenseNet-121, EfficientNetV2-S, ResNet-50, Inception-V3) integrated with Squeeze-and-Excitation (SE) atte...

What's in a NAM?

The transition away from animal experimentation is gaining momentum across scientific disciplines, driven by technological advancements, ethical imperatives and policy support. At the forefront of this movement is the development of New Approach Methodologies (NAMs). Rather than serving as mere substitutes for animal testing, NAMs represent a paradigm shift. Their growing acceptance has been reinf...

Optimizing mild traumatic brain injury (mTBI) triage: A comparative analysis of CEREBO® vs clinical prediction in reducing CT utilization and improving cost-effectiveness.

OBJECTIVE: Clinical Prediction Rules (CPR) such as the New Orleans Criteria (NOC), National Institute for Health and Clinical Excellence (NICE), and C...

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 ...

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 ident...

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...

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...

Reader comment regarding machine learning models to detect opioid misuse in emergency department patients at triage.

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...

The authors respond to reader comment regarding machine learning models to detect opioid misuse in emergency department patients at triage.