Emergency Medicine

The application of artificial intelligence in the management of sepsis.

Sepsis is a complex and heterogeneous syndrome that remains a serious challenge to healthcare worldw...

Informed consent for artificial intelligence in emergency medicine: A practical guide.

As artificial intelligence (AI) expands its presence in healthcare, particularly within emergency me...

Prognostication of Outcomes in Spontaneous Intracerebral Hemorrhage: A Propensity Score-Matched Analysis with Support Vector Machine.

OBJECTIVE: The role of surgery in spontaneous intracerebral hemorrhage (SICH) remains controversial....

Deep-Learning Automation of Preoperative Radiographic Parameters Associated With Early Periprosthetic Femur Fracture After Total Hip Arthroplasty.

BACKGROUND: The radiographic assessment of bone morphology impacts implant selection and fixation ty...

Assessing the Potential of a Deep Learning Tool to Improve Fracture Detection by Radiologists and Emergency Physicians on Extremity Radiographs.

RATIONALE AND OBJECTIVES: To evaluate the standalone performance of a deep learning (DL) based fract...

Automatic segmentation of inconstant fractured fragments for tibia/fibula from CT images using deep learning.

Orthopaedic surgeons need to correctly identify bone fragments using 2D/3D CT images before trauma s...

Enhancing efficiency and capacity of telehealth services with intelligent triage: a bidirectional LSTM neural network model employing character embedding.

BACKGROUND: The widespread adoption of telehealth services necessitates accurate online department s...

Implanting deep learning models for burn wound assessment.

Classification of rib fracture types from postmortem computed tomography images using deep learning.

Human or time resources can sometimes fall short in medical image diagnostics, and analyzing images ...

Weakly supervised deep learning for diagnosis of multiple vertebral compression fractures in CT.

OBJECTIVE: This study aims to develop a weakly supervised deep learning (DL) model for vertebral-lev...

An extended focused assessment with sonography in trauma ultrasound tissue-mimicking phantom for developing automated diagnostic technologies.

Medical imaging-based triage is critical for ensuring medical treatment is timely and prioritized. ...

Development of AI-Based Diagnostic Algorithm for Nasal Bone Fracture Using Deep Learning.

Facial bone fractures are relatively common, with the nasal bone the most frequently fractured facia...

Fusion of electronic health records and radiographic images for a multimodal deep learning prediction model of atypical femur fractures.

Atypical femur fractures (AFF) represent a very rare type of fracture that can be difficult to discr...

Optimization of laccase from Stenotrophomonas maltophilia E1 by submerge fermentation using coconut husk with its detoxification and biodecolorization ability of synthetic dyes.

Enzymatic degradation of synthetic dyes holds an immense promise for addressing the environmental co...

Artificial intelligence and machine learning for clinical pharmacology.

Artificial intelligence (AI) will impact many aspects of clinical pharmacology, including drug disco...

Prediction of cerebral hemorrhagic transformation after thrombectomy using a deep learning of dual-energy CT.

OBJECTIVES: To develop and validate a deep learning model for predicting hemorrhagic transformation ...

LFighter: Defending against the label-flipping attack in federated learning.

Federated learning (FL) provides autonomy and privacy by design to participating peers, who cooperat...

How AI is advancing asthma management? Insights into economic and clinical aspects.

Asthma, an increasingly prevalent chronic respiratory condition, incurs significant economic costs w...

Predicting root fracture after root canal treatment and crown installation using deep learning.

BACKGROUND/PURPOSE: Vertical root fracture (VRF) is a prevalent reason for tooth extraction followin...

Outcome prediction of methadone poisoning in the United States: implications of machine learning in the National Poison Data System (NPDS).

Methadone is an opioid receptor agonist with a high potential for abuse. The current study aimed to ...

Prediction of early hematoma expansion of spontaneous intracerebral hemorrhage based on deep learning radiomics features of noncontrast computed tomography.

OBJECTIVES: Aimed to develop a nomogram model based on deep learning features and radiomics features...