Emergency Medicine

SuperLearner approach for predicting imminent risk of fracture in older Chinese patients with newly diagnosed osteoporosis based on their routine blood test markers.

Advancing nanomedicine with machine learning: predicting protein corona and nano-bio interactions.

The protein corona (PC) has widely been demonstrated to impact the pharmacokinetics, safety, and efficiency of nanoparticle (NP) systems for clinical applications such as drug or vaccine delivery. However, a comprehensive understanding of PC formation and its overall impact on biological behavior remains challenging due to the wide range of possible physicochemical and experimental parameters that...

From prediction to practice: early implementation of a machine learning-based hospitalization prediction tool in the emergency department.

BACKGROUND: Machine learning (ML) models can accurately predict hospital admissions in emergency departments (EDs), but real-world adoption remains ra...

Artificial Intelligence-assisted Periapical Radiographic Assessment: Lesion Detection, Endodontic Complication Analysis, and Review of Clinical Treatment Recommendations.

INTRODUCTION: Artificial intelligence (AI) systems are increasingly used in dental radiology to support endodontic diagnosis. However, their diagnosti...

Identification and Experimental Validation of Biomarkers Associated with PI3K/AKT Signaling Pathway in Spinal Cord Injury.

Spinal cord injury (SCI) refers to trauma to the spinal cord resulting in functional deficits. Dysregulation of the phosphoinositide 3-kinase/serine-t...

An AI Based Screening Tool for Detection of Cerebral Venous Thrombosis on Non-Contrast Brain CT.

While non-contrast computed tomography (NCCT) is the primary ‎imaging modality in emergency settings, it has a low sensitivity for detection of ‎Cereb...

Design of a soft robotic endoscope with enhanced bending and AI-based prediction.

Minimally invasive surgery (MIS) increasingly benefits from soft robotic platforms that offer enhanced compliance and safer human-robot interaction. H...

Cerebral Vasospasm Detection and Delayed Cerebral Ischemia Prediction after Aneurysmal Subarachnoid Hemorrhage: A Scoping Review.

BACKGROUND/OBJECTIVE: Aneurysmal subarachnoid hemorrhage (aSAH) is complicated by angiographic cerebral vasospasm and delayed cerebral ischemia (DCI),...

Machine learning predicts ICU mortality in cardiogenic shock: interpreting SOFA, lactate, and pH thresholds.

Visit-to-visit systolic blood pressure variability and risk of severe hypertensive episodes in a multicenter cohort.

BACKGROUND: Visit-to-visit blood pressure variability has been associated with cardiovascular outcomes; however, its role in predicting severe blood p...

Development and Validation of a Machine Learning Model for Predicting Postoperative Delirium in Elderly Patients with Hip Fracture: A Retrospective Cohort Study.

Postoperative delirium (POD) is a common and serious complication in elderly hip fracture patients, including those undergoing surgery for femoral nec...

Physics-informed deep learning for rapid marine radionuclide dispersion forecasting in nuclear emergency response.

In the event of a severe nuclear accident at a coastal nuclear power plant, the rapid and accurate assessment of radionuclide dispersion in surroundin...

Addressing diagnostic code variability in intimate partner violence surveillance through natural language processing: Evidence from substance use disorder populations.

BACKGROUND: Intimate partner violence (IPV) and substance use disorders (SUDs) represent major public health challenges, yet identifying IPV cases in ...

The role of artificial intelligence-enhanced multispectral imaging in burn surgery decision-making: A patient-centered mixed-methods service evaluation.

BACKGROUND: Artificial intelligence (AI) supported imaging is increasingly used across plastic surgery, including burn care, to assist with assessment...

GUIDE-US: grade-informed unpaired distillation of encoder knowledge from histopathology to micro-ultrasound.

PURPOSE: Non-invasive grading of prostate cancer (PCa) from micro-ultrasound (micro-US) could expedite triage and guide biopsies toward the most aggre...

Integrated multi-omics data and machine learning approaches to decipher the molecular network and gene signatures of renal cell carcinoma induced by aristolochic acid.

OBJECTIVE: This study aims to delineate the molecular mechanisms through which aristolochic acid (AA) exposure drives renal cell carcinoma (RCC) patho...

Natural Language Processing for Surgical Quality Enhancement.

INTRODUCTION: Unstructured clinical data, including operative reports and progress notes, contain critical surgical information that is often absent f...

Development and Validation of Machine Learning Models to Identify Emergency Department Patients at Increased Risk of New or Progressive Acute Kidney Injury.

OBJECTIVES: Acute kidney injury (AKI) is a common and serious condition associated with prolonged hospitalization, chronic kidney disease, and increas...

Ensemble learning for predicting birth trauma using high-dimensional data in the neonatal intensive care unit.

Birth trauma remains a significant global health challenge and contributes substantially to neonatal morbidity and mortality. Although machine learnin...

[Advances in oral and craniofacial bone regeneration modulated by stem cells and biomaterials].

Cranio-maxillofacial bone defects resulting from trauma, tumors, infection, or congenital malformations not only severely impair patients' physiologic...