Emergency Medicine

Development of machine learning models to prognosticate chronic shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage.

BACKGROUND: Shunt-dependent hydrocephalus significantly complicates subarachnoid hemorrhage (SAH), and reliable prognosis methods have been sought in recent years to reduce morbidity and costs associated with delayed treatment or neglected onset. Machine learning (ML) defines modern data analysis techniques allowing accurate subject-based risk stratifications. We aimed at developing and testing di...

Identifying Fatal Head Injuries on Postmortem Computed Tomography Using Convolutional Neural Network/Deep Learning: A Feasibility Study.

Postmortem computed tomography (PMCT) is a relatively recent advancement in forensic pathology practice that has been increasingly used as an ancillary investigation and screening tool. One area of clinical CT imaging that has garnered a lot of research interest recently is the area of "artificial intelligence" (AI), such as in screening and computer-assisted diagnostics. This feasibility study in...

The first use of artificial intelligence (AI) in the ER: triage not diagnosis.

Predictions related to the impact of AI on radiology as a profession run the gamut from AI putting radiologists out of business to having no effect at...

Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: Machine learning approach.

OBJECTIVES: We aimed to build a machine learning predictive model to predict the risk of prolonged mechanical ventilation (PMV) for patients with Trau...

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine.

Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development...

Artificial intelligence method to classify ophthalmic emergency severity based on symptoms: a validation study.

OBJECTIVES: We investigated the usefulness of machine learning artificial intelligence (AI) in classifying the severity of ophthalmic emergency for ti...

Machine learning prediction of stone-free success in patients with urinary stone after treatment of shock wave lithotripsy.

BACKGROUND: The aims of this study were to determine the predictive value of decision support analysis for the shock wave lithotripsy (SWL) success ra...

Agents and robots for collaborating and supporting physicians in healthcare scenarios.

Monitoring patients through robotics telehealth systems is an interesting scenario where patients' conditions, and their environment, are dynamic and ...

Utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of PTSD following trauma exposure.

BACKGROUND: Though lifetime exposure to traumatic events is significant, only a minority of individuals develops symptoms of posttraumatic stress diso...

Human-computer collaboration for skin cancer recognition.

The rapid increase in telemedicine coupled with recent advances in diagnostic artificial intelligence (AI) create the imperative to consider the oppor...

An Approach to Biomarker Discovery of Cannabis Use Utilizing Proteomic, Metabolomic, and Lipidomic Analyses.

Relatively little is known about the molecular pathways influenced by cannabis use in humans. We used a multi-omics approach to examine protein, meta...

Automated Lung Ultrasound B-Line Assessment Using a Deep Learning Algorithm.

Shortness of breath is a major reason that patients present to the emergency department (ED) and point-of-care ultrasound (POCUS) has been shown to ai...

Artificial intelligence and radiomics in pediatric molecular imaging.

In the past decade, a new approach for quantitative analysis of medical images and prognostic modelling has emerged. Defined as the extraction and ana...

Predicting direct hepatocyte toxicity in humans by combining high-throughput imaging of HepaRG cells and machine learning-based phenotypic profiling.

Accurate prediction of drug- and chemical-induced hepatotoxicity remains to be a problem for pharmaceutical companies as well as other industries and ...

Toward automatic C-arm positioning for standard projections in orthopedic surgery.

PURPOSE: Guidance and quality control in orthopedic surgery increasingly rely on intra-operative fluoroscopy using a mobile C-arm. The accurate acquis...

A novel model for predicting the outcome of intracerebral hemorrhage: Based on 1186 Patients.

OBJECTIVE: To establish a model for predicting the outcome according to the clinical and computed tomography(CT) image data of patients with intracere...

Utilization of a Deep Learning Algorithm for Microscope-Based Fatty Vacuole Quantification in a Fatty Liver Model in Mice.

Quantification of fatty vacuoles in the liver, with differentiation from lumina of liver blood vessels and bile ducts, is an example where the traditi...

Effectiveness of robot-assisted gait training on patients with burns: a preliminary study.

Gait enables individuals to move forward and is considered a natural skill. However, gait disturbances are very common in patients with burn injury. R...

Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach.

BACKGROUND: The use of machine learning techniques to predict diseases outcomes has grown significantly in the last decade. Several studies prove that...

Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review.

Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such ...