Emergency Medicine

Development and validation of a novel orthopedic blood use prediction model incorporating lab tests and thromboelastography.

OBJECTIVE: To develop and validate a prediction model integrating laboratory parameters and thromboelastography (TEG) for forecasting blood transfusion needs in orthopedic surgery. METHODS: This retrospective study enrolled 250 patients undergoing joint replacement, spinal fusion, or fracture fixation. Participants were randomized into training (n = 175) and validation (n = 75) sets. Preoperative ...

Artificial intelligence powered mobile health apps for skin cancer detection: current challenges and a systems thinking approach for improved public health outcomes in low- and middle-income countries.

Artificial intelligence (AI) powered mobile health (mHealth) apps are emerging as vital self-triage tools for skin cancer detection. By utilizing smartphone cameras, these apps analyze skin lesions to assess the risk and provide tailored care recommendations, ranging from self-care guidance to directing users to appropriate healthcare providers. While this positively impacts Sustainable Developmen...

Comparative Performance of YOLO Models in the Automated Segmentation of Dental Traumas on Panoramic Radiographs.

OBJECTIVE: This study aimed to compare the performance of artificial intelligence-based deep convolutional neural networks, YOLOv8, YOLOv11, and YOLOv...

Deep Learning Model for Differentiating Between Neoplastic Pathologic Fracture and Nonpathologic Fracture Using Hip Radiographs.

BACKGROUND: Although radiographs are the first-line imaging modality, differentiating between neoplastic pathologic fractures and nonpathologic fractu...

An Algorithm to Avoid Missed Bowel Injuries in Blunt Abdominal Trauma Patients.

UNLABELLED: Bowel injuries following blunt abdominal trauma (BAT) are evasive to detect clinically and with current imaging modalities. These injuries...

Diagnostic performance of artificial intelligence for facial fracture detection: a systematic review.

OBJECTIVE: To evaluate the diagnostic performance of artificial intelligence (AI) models for detecting facial bone fractures on computed tomography (C...

Artificial Intelligence Technologies Supporting Nurses' Clinical Decision-Making: A Systematic Review.

BACKGROUND: The use of technology to support nurses' decision-making is increasing in response to growing healthcare demands. AI, a global trend, hold...

Hereditary syndromes and RCC: what radiologists need to know.

Hereditary renal cell carcinoma (RCC) accounts for approximately 5-8% of all renal cancers. This review provides a comprehensive overview of the seven...

Artificial intelligence‑based quantitative analysis of hepatic fibrosis in carbon tetrachloride-induced mouse model of metabolic dysfunction-associated steatohepatitis.

UNLABELLED: Liver fibrosis, a major histopathological indicator of chronic liver injury, is also a key feature of metabolic dysfunction-associated ste...

Reveal a risk and potential mechanism for keratoconus progression with isotretinoin: Toxicogenomic, transcriptomics and real-world evidence insights.

PURPOSE: Limited clinical data and unstable animal models hinder understanding isotretinoin-induced keratoconus. We addressed this by integrating phar...

Artificial intelligence for mortality risk stratification in septic shock: A systematic review and meta-analysis.

BACKGROUND: Septic shock is associated with high mortality, making accurate risk stratification crucial for tailored treatment. Traditional clinical s...

Machine learning identifies traumatic experiences as being associated with auditory verbal hallucinations in both a non-clinical population and individuals diagnosed with psychosis.

BACKGROUND: Auditory verbal hallucinations (AVHs) are a core symptom of psychosis but their prevalence in the general population ranges from 5-28 %. M...

Interpretable machine learning framework for predicting pesticide phytotoxicity in wastewater reuse: Integrating molecular, quantum, and experimental descriptors.

Pesticides are essential for crop protection, but their potential toxicity poses significant environmental and health risks. Although numerous toxicol...

Diagnostic performance of X-ray-based deep learning models for detecting ankle and foot fractures: a systematic review and meta-analysis.

OBJECTIVES: Lower extremity fractures are prevalent in vulnerable populations leading to significant burdens, which highlight the need for timely and ...

An explainable machine learning model predicts 30-day readmission after vertebral augmentation.

Osteoporotic vertebral compression fracture (OVCF) patients face high 30-day readmission risks after vertebral augmentation procedures (VAPs). Using e...

From passive prevention to proactive intervention: An AI-assisted integrated governance system for algal bloom monitoring and control.

Algal blooms, characterized by the excessive proliferation of microalgae in a freshwater or marine ecosystem, have evolved into a global ecological, h...

Ambient Artificial Intelligence Versus Human Scribes in the Emergency Department.

STUDY OBJECTIVE: To compare the use of ambient artificial intelligence (AI) versus human scribes in the emergency department in terms of note quality ...

Machine learning models for predicting hospital admission in pediatric emergency departments: A systematic review.

CONTEXT: Pediatric Emergency Departments (PEDs) face overcrowding partially due to delayed hospital admission decision. Machine Learning (ML) models c...

The effect of robot-assisted training on burn rehabilitation: A systematic review and meta-analysis.

OBJECTIVE: This study aimed to systematically evaluate the efficacy of robot-assisted training (RAT) in improving the physical rehabilitation outcomes...

Advancements in burn size assessment: A systematic review of emerging technologies.

BACKGROUND: Accurate and reproducible assessment of total body surface area (TBSA) in burns is vital for guiding fluid resuscitation, triage, referral...