Emergency Medicine

Developing a predictive model for septic shock risk in acute pancreatitis patients using interpretable machine learning algorithms.

BACKGROUND: Septic shock is a severe complication of acute pancreatitis (AP), often associated with poor prognosis. This study aims to analyze the clinical characteristics of patients with acute pancreatitis and develop an interpretable early prediction model for septic shock in these patients using machine learning (ML). The model is intended to assist emergency physicians in resource allocation ...

Bridging Genomic Research Disparities in Osteoporosis GWAS: Insights for Diverse Populations.

PURPOSE OF REVIEW: Genome-wide association studies (GWAS) have significantly advanced osteoporosis research by identifying genetic loci associated with bone mineral density (BMD) and fracture risk. However, disparities persist due to the underrepresentation of non-European populations, limiting the applicability of polygenic risk scores (PRS). This review examines recent advancements in osteoporos...

Detection, Classification, and Segmentation of Rib Fractures From CT Data Using Deep Learning Models: A Review of Literature and Pooled Analysis.

PURPOSE: Trauma-induced rib fractures are common injuries. The gold standard for diagnosing rib fractures is computed tomography (CT), but the sensiti...

Optimizing the power of AI for fracture detection: from blind spots to breakthroughs.

Artificial Intelligence (AI) is increasingly being integrated into the field of musculoskeletal (MSK) radiology, from research methods to routine clin...

A Joint Geometric Topological Analysis Network (JGTA-Net) for Detecting and Segmenting Intracranial Aneurysms.

OBJECTIVE: The rupture of intracranial aneurysms leads to subarachnoid hemorrhage. Detecting intracranial aneurysms before rupture and stratifying the...

Development and validation of a multi-omics hemorrhagic transformation model based on hyperattenuated imaging markers following mechanical thrombectomy.

This study aimed to develop a predictive model integrating clinical, radiomics, and deep learning (DL) features of hyperattenuated imaging markers (HI...

Development of a non-contrast CT-based radiomics nomogram for early prediction of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage.

BACKGROUNDS: Delayed cerebral ischemia (DCI) is a significant complication following aneurysmal subarachnoid hemorrhage (aSAH), leading to poor progno...

Investigating the Mechanisms of Pazopanib-induced Hepatotoxicity: Insights from Network Toxicology, Microarray Analysis, and Machine Learning.

BACKGROUND: Pazopanib is an oral multi-kinase inhibitor that is effective in treating various tumors. However, it is commonly associated with hepatoto...

Deep Learning-Based Aortic Diameter Measurement in Traumatic Hemorrhage Using Shallow Attention Network: A Path Forward.

The accurate assessment of aortic diameter (AoD) is essential in managing patients with traumatic hemorrhage, particularly during interventions such ...

Using Traditional and Deep Machine Learning to Predict Emergency Room Triage Levels.

Accurate triage in emergency rooms is crucial for efficient patient care and resource allocation. We developed methods to predict triage levels using ...

HealthiVert-GAN: A Novel Framework of Pseudo-Healthy Vertebral Image Synthesis for Interpretable Compression Fracture Grading.

Osteoporotic vertebral compression fractures (OVCFs) are prevalent in the elderly population, typically assessed on computed tomography (CT) scans by ...

Machine learning models in the prediction of chronic or shunt-dependent hydrocephalus following subarachnoid hemorrhage: A systematic review and meta-analysis.

PurposeChronic or shunt-dependent hydrocephalus is a frequent consequence of subarachnoid hemorrhage (SAH) with an unclear pathophysiology, making tre...

Temporary circulatory support for cardiogenic shock.

PURPOSE OF REVIEW: Temporary circulatory support (TCS) devices play a crucial role in stabilizing patients with refractory cardiogenic shock. They pro...

Performance of multimodal prediction models for intracerebral hemorrhage outcomes using real-world data.

BACKGROUND: We aimed to develop and validate multimodal models integrating computed tomography (CT) images, text and tabular clinical data to predict ...

Genetic Risk Scores in Stroke Research and Care.

Stroke remains a leading cause of death and disability worldwide. While well-established risk factors play a major role, genetic predisposition is a c...

Systematic review on the impact of deep learning-driven worklist triage on radiology workflow and clinical outcomes.

OBJECTIVES: To perform a systematic review on the impact of deep learning (DL)-based triage for reducing diagnostic delays and improving patient outco...

A Machine Learning Method for Predicting Acute Kidney Injury in Patients with Intracranial Hemorrhage.

Intracranial hemorrhage (ICH) is a critical and urgent condition in clinical practice. Recent research has highlighted acute kidney injury (AKI) that ...

Promoting patient health literacy in burn care through artificial intelligence language learning models: A study of text analysis and simplification.

Health literacy is essential in patient care, especially in burn treatment, where understanding care information can significantly influence recovery ...

Data-driven identification of urgent surgical procedures for use in trauma outcomes measurement.

BACKGROUND: No standardized list of urgent-trauma-surgery exists for analysis in injury studies. If coded by a standard classification system, such a ...

Cerebral ischemia detection using deep learning techniques.

Cerebrovascular accident (CVA), commonly known as stroke, stands as a significant contributor to contemporary mortality and morbidity rates, often lea...