Emergency Medicine

A Meta-Analysis of the Diagnosis of Condylar and Mandibular Fractures Based on 3-dimensional Imaging and Artificial Intelligence.

This article aims to review the literature, study the current situation of using 3D images and artificial intelligence-assisted methods to improve the rapid and accurate classification and diagnosis of condylar fractures and conduct a meta-analysis of mandibular fractures. Mandibular condyle fracture is a common fracture type in maxillofacial surgery. Accurate classification and diagnosis of condy...

Application of Transformer Neural Networks for Data Cleaning in Emergency Room Logs: A Case Study from the Bordeaux University Hospital.

Emergency Rooms (ERs) are at the center of various optimization research due to the growing number of visits in recent decades. The accurate logging of patient movement and time spent in ERs is essential for studying patient pathways and improving operations. Despite the innovative digital tracking system employed at the Bordeaux University Hospital, over 90% of these logs suffer from missing or i...

Deep Learning-Enhanced Hand-Driven Spatial Encoding Microfluidics for Multiplexed Molecular Testing at Home.

The frequent global outbreaks of viral infectious diseases have significantly heightened the urgent demand for molecular testing at home. However, the...

Prediction of Internal Exposures after Virtual Oral Doses of Disparate Chemicals in Rats and Humans Using Simplified Physiologically Based Pharmacokinetic Models with Generated Input Parameters.

Toxicological evaluation of industrial chemicals with a broad range of chemical structures, for example, bioactive food components, toxic food-derived...

Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in trauma-induced coagulopathy.

Despite advancements in trauma care, uncontrolled hemorrhage and trauma-induced coagulopathy (TIC) remain the leading causes of preventable deaths aft...

Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22.

Artificial Intelligence (AI) is rapidly transforming the landscape of critical care, offering opportunities for enhanced diagnostic precision and pers...

Mechanobiology-guided machine learning models for predicting long bone fracture healing across diverse scenarios.

BACKGROUND: Fracture healing is a complex, time-dependent process governed by biological and mechanical factors, including implant properties. While f...

Promising for patients or deeply disturbing? The ethical and legal aspects of deepfake therapy.

Deepfakes are hyper-realistic but fabricated videos created with the use of artificial intelligence. In the context of psychotherapy, the first studie...

Impact of a computed tomography-based artificial intelligence software on radiologists' workflow for detecting acute intracranial hemorrhage.

PURPOSE: To assess the impact of a commercially available computed tomography (CT)-based artificial intelligence (AI) software for detecting acute int...

Machine learning-based prediction of short- and long-term mortality for shared decision-making in older hip fracture patients: the Dutch Hip Fracture Audit algorithms in 74,396 cases.

BACKGROUND AND PURPOSE:  Treatment-related shared decision-making (SDM) in older adults with hip fractures is complex due to the need to balance patie...

Discrimination of Dengue Diseases in Children Using Surface-Enhanced Raman Spectroscopy Coupled with Machine Learning Approaches.

This study introduces a novel approach to dengue diagnostics by leveraging surface-enhanced Raman spectroscopy (SERS) coupled to machine learning. Thi...

External validation of an artificial intelligence tool for fracture detection in children with osteogenesis imperfecta: a multireader study.

OBJECTIVE: To determine the performance of a commercially available AI tool for fracture detection when used in children with osteogenesis imperfecta ...

Improving prediction of fragility fractures in postmenopausal women using random forest.

Osteoporosis is a chronic disease characterized by a progressive decline in bone density and quality, leading to increased bone fragility and a higher...

Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm rupture.

Small intracranial aneurysms (SIAs) (< 5 mm) are increasingly detected due to advanced imaging, but predicting rupture risk remains challenging. Ruptu...

Development of a Risk Prediction Model for Linezolid-Induced Thrombocytopenia Based on the Machine Learning Algorithm.

The research aimed to develop a validated model for predicting the risk of linezolid-induced thrombocytopenia (LIT). An XGBoost model and SelectFromMo...

Identifying features of prior hemorrhage in cerebral cavernous malformations on quantitative susceptibility maps: a machine learning pilot study.

Features of new bleeding on conventional imaging in cerebral cavernous malformations (CCMs) often disappear after several weeks, yet the risk of reble...

Assessing risk of bias in toxicological studies in the era of artificial intelligence.

Risk of bias is a critical factor influencing the reliability and validity of toxicological studies, impacting evidence synthesis and decision-making ...

Predicting ESWL success for ureteral stones: a radiomics-based machine learning approach.

OBJECTIVES: This study aimed to develop and validate a machine learning (ML) model that integrates radiomics and conventional radiological features to...

Experimental and machine learning-driven assessment of IS 2062 steel under double-base propellant combustion conditions.

The degradation behavior of IS 2062 structural steel under extreme combustion conditions induced by double-base propellant exposure is crucial for aer...

How well does GPT-4 perform on an emergency medicine board exam? A comparative assessment.

BACKGROUND: Recent advancements in artificial intelligence have shown promise in enhancing diagnostic precision within healthcare sectors. In emergenc...