Emergency Medicine

Landmark-based deep learning for radiographic screening for developmental dysplasia of the hip in infants: Development and external evaluation with IHDI-guided triage.

BACKGROUND: In Japan, secondary screening for developmental hip dysplasia has expanded. However, the capacity of screening programs has outpaced the availability of ultrasonography and the number of clinicians who perform and interpret examinations outside tertiary centers. Plain radiography is widely accessible; however, interpreting images in infants can be challenging. This study developed and ...

Optimization and External Validation of a Deep Learning Model for Segmentation and Quantification of Traumatic Brain Injury Lesions on Head Computed Tomography.

Current methods for radiological classification in traumatic brain injury (TBI), such as the Marshall and Rotterdam score, provide an incomplete description of intracranial lesion burden, rely on time-consuming manual assessments by experts, and are prone to intra- and interrater disagreement. To circumvent these limitations, we previously proposed the Brain Lesion Analysis and Segmentation Tool f...

Where New and Old Technologies Converge: In Search of a Better Way to Predict Pathologic Fracture: Commentary on an article by Shinn Kim, MD, et al.: "Deep Learning Model for Differentiating Between Neoplastic Pathologic Fracture and Nonpathologic Fracture Using Hip Radiographs".

Using participatory action research to develop an artificial intelligence-augmented, peer-driven, case-based, and simulation-enhanced curriculum for emergency medicine residents.

This work describes the use of participatory action research to develop an artificial intelligence (AI)-augmented, peer-driven, case-based, and simula...

Can human-aware agentic artificial intelligence transform emergency care workflows?

Uncovering the potential mechanisms of aspartame-induced oral leukoplakia via integrated network toxicology and molecular dynamics simulations.

OBJECTIVE: Aspartame, a widely used artificial sweetener, has attracted increasing attention for its potential systemic and oral toxicity. However, th...

Comparison of Artificial Intelligence Chatbots and Dental Students on Context of Dental Trauma.

AIM: Daily use of chatbots has become widespread in dental education just as in every other field. Dental students consult them when performing tasks ...

Artificial sweeteners and male infertility: Network toxicology and experimental evidence reveal FGFR1 as the critical target.

BACKGROUND: Artificial sweeteners (ASs) are widely used sugar substitutes, but chronic exposure is linked to male infertility. We integrated computati...

Artificial intelligence-driven triage and decision-making in trauma systems/settings.

PURPOSE OF REVIEW: Trauma care demands rapid decision-making under uncertainty and time pressure, where maintaining situational awareness becomes chal...

Building trust in the integration of artificial intelligence into chemical risk assessment: findings from the 2024 ECETOC workshop.

Artificial Intelligence (AI) is increasingly influencing chemical risk assessment, enabling faster, more comprehensive, and potentially more ethical a...

Diethyl Phthalate (DEP) as a potential osteosarcoma risk factor: a multi-omics study integrating network Toxicology, single-cell RNA sequencing, and molecular docking.

Diethyl phthalate (DEP), a common plasticiser and endocrine disruptor, has been linked to cancer, but its role in osteosarcoma (OS) remains unclear. T...

Exploring estrogen synthesis and metabolism related markers and immune dynamics in osteoporosis-insights from single-cell RNA sequencing and Mendelian randomization.

BACKGROUND: Osteoporosis (OP) is characterized by decreased bone mineral density and an increased fracture risk, often due to declining sex hormone le...

Artificial intelligence meets point-of-care ultrasound: implications for pediatric emergency and critical care.

PURPOSE OF REVIEW: Artificial intelligence has been integrated in nearly all aspects of clinical care to improve patient outcomes, augment human capab...

Mammalian AHR2 loss: Evolutionary patterns, mechanisms, and relevance of biological factors.

Environmental contamination by dioxins and polycyclic aromatic hydrocarbons threatens wildlife health, yet the molecular basis of interspecies variati...

Machine Learning Methods to Identify Pediatric Shunt Malfunction in the Acute Care Setting and the Development of ShuntGPT.

BACKGROUND AND OBJECTIVES: Identifying predictors of shunt failure in acute care settings is challenging. Previous studies largely rely on traditional...

Development and comparison of machine learning models for predicting postoperative ileus after posterior thoracolumbar fracture surgery.

In the artificial intelligence age, is Oswestry spinal risk index still useful? an external validation of Oswestry spinal risk index applied to radiotherapy patients and its relevance.

BACKGROUND AND STUDY AIMS: In 2013, Balain et al. proposed a scoring system, the Oswestry Spinal Risk Index (OSRI), to predict survival in patients wi...

Approach to Optimizing Tranexamic Acid Use in Trauma: Potential Utilization of Trauma Phenotypes.

Tranexamic acid (TXA) reduces mortality in patients with trauma; however, optimal patient selection remains unclear. This study aimed to identify trau...

Emerging Roles of Multiparametric Biodosimetry and Artificial Intelligence in Advancing Radiation Dose Assessment for Triage.

Biodosimetry plays a crucial role in radiation emergency preparedness and response by enabling efficient allocation of medical resources through prior...

Deep learning inversion of water content and relaxation time in water-bearing fracture zones based on surface NMR data.