Emergency Medicine

Can DXA image-based deep learning model predict the anisotropic elastic behavior of trabecular bone?

3D image-based finite element (FE) and bone volume fraction (BV/TV)/fabric tensor modeling techniques are currently used to determine the apparent stiffness tensor of trabecular bone for assessing its anisotropic elastic behavior. Inspired by the recent success of deep learning (DL) techniques, we hypothesized that DL modeling techniques could be used to predict the apparent stiffness tensor of tr...

Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review.

Since whole-slide imaging has been commercially available for over 2 decades, digital pathology has become a constantly expanding aspect of the pathology profession that will continue to significantly impact how pathologists conduct their craft. While some aspects, such as whole-slide imaging for archiving, consulting, and teaching, have gained broader acceptance, other facets such as quantitative...

Robotic mesh-supported pectopexy for pelvic organ prolapse: expanding the options of pelvic floor repair.

Pelvic organ prolapse affects 30-50% of the female population. For definitive treatment surgery is unavoidable. Sacrocolpopexy has been the gold stand...

Machine learning based early mortality prediction in the emergency department.

BACKGROUND: It is a great challenge for emergency physicians to early detect the patient's deterioration and prevent unexpected death through a large ...

Use of an artificial intelligence-based rule extraction approach to predict an emergency cesarean section.

OBJECTIVE: One of the major problems with artificial intelligence (AI) is that it is generally known as a "black box". Therefore, the present study ai...

Deep Learning-Based Natural Language Processing in Radiology: The Impact of Report Complexity, Disease Prevalence, Dataset Size, and Algorithm Type on Model Performance.

In radiology, natural language processing (NLP) allows the extraction of valuable information from radiology reports. It can be used for various downs...

Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients.

BACKGROUND: A deep learning model (DLM) that enables non-invasive hypokalemia screening from an electrocardiogram (ECG) may improve the detection of t...

EAD-Net: A Novel Lesion Segmentation Method in Diabetic Retinopathy Using Neural Networks.

Diabetic retinopathy (DR) is a common chronic fundus disease, which has four different kinds of microvessel structure and microvascular lesions: micro...

Physically crosslinked PVA/graphene-based materials/aloe vera hydrogel with antibacterial activity.

Burn is a major skin injury that occurs worldwide. For second-degree burns, special treatment should be given for creating a suitable wound healing en...

Machine learning models of ischemia/hemorrhage in moyamoya disease and analysis of its risk factors.

OBJECT: This study aimed to determine the risk factors of ischemic/hemorrhagic stroke in patients suffering moyamoya disease (MMD), as well as to comp...

Exposure to polydopamine nanoparticles induces neurotoxicity in the developing zebrafish.

Currently, the potential applications of polydopamine (PDA) nanoparticles in the biomedical field are being extensively studied, such as cell internal...

CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes.

The vast majority of approved drugs are metabolized by the five major cytochrome P450 (CYP) isozymes, 1A2, 2C9, 2C19, 2D6 and 3A4. Inhibition of CYP i...

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis.

The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes i...

Artificial intelligence-enhanced echocardiography in the emergency department.

A focused cardiac ultrasound performed by an emergency physician is becoming part of the standard assessment of patients in a variety of clinical situ...

Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review.

BACKGROUND: Chest pain is amongst the most common reason for presentation to the emergency department (ED). There are many causes of chest pain, and i...

Human inspired fall arrest strategy for humanoid robots based on stiffness ellipsoid optimisation.

Falls are a common risk and impose severe threats to both humans and humanoid robots as a product of bipedal locomotion. Inspired by human fall arrest...

A real-world demonstration of machine learning generalizability in the detection of intracranial hemorrhage on head computerized tomography.

Machine learning (ML) holds great promise in transforming healthcare. While published studies have shown the utility of ML models in interpreting medi...

Fully Automated Deep Learning Tool for Sarcopenia Assessment on CT: L1 Versus L3 Vertebral Level Muscle Measurements for Opportunistic Prediction of Adverse Clinical Outcomes.

Sarcopenia is associated with adverse clinical outcomes. CT-based skeletal muscle measurements for sarcopenia assessment are most commonly performed ...

CXCL1: A new diagnostic biomarker for human tuberculosis discovered using Diversity Outbred mice.

More humans have died of tuberculosis (TB) than any other infectious disease and millions still die each year. Experts advocate for blood-based, serum...

Large Vessel Occlusion Prediction in the Emergency Department with National Institutes of Health Stroke Scale Components: A Machine Learning Approach.

OBJECTIVE: To determine the feasibility of using a machine learning algorithm to screen for large vessel occlusions (LVO) in the Emergency Department ...