Domestic Violence

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 all. The use of AI appears to show significant promise in ER triage in the present. We briefly discuss the emerging effectiveness of AI in the ER imaging setting by looking at some of the products approved by the FDA and finding their way into "prac...

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 Traumatic Brain Injury (TBI).

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...

A preliminary evaluation of still face images by deep learning: A potential screening test for childhood developmental disabilities.

Most developmental disorders are defined by their clinical symptoms and many disorders share common features. The main objective of this research is t...

An innovative method for screening and evaluating the degree of diabetic retinopathy and drug treatment based on artificial intelligence algorithms.

Current methods of evaluating the degree of diabetic retinopathy are highly subjective and have no quantitative standard. To objectively evaluate the ...

Improvement of oral cancer screening quality and reach: The promise of artificial intelligence.

Oral cancer is easily detectable by physical (self) examination. However, many cases of oral cancer are detected late, which causes unnecessary morbid...

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...

Fully Automated Segmentation of Globes for Volume Quantification in CT Images of Orbits using Deep Learning.

BACKGROUND AND PURPOSE: Fast and accurate quantification of globe volumes in the event of an ocular trauma can provide clinicians with valuable diagno...

A high-throughput system combining microfluidic hydrogel droplets with deep learning for screening the antisolvent-crystallization conditions of active pharmaceutical ingredients.

Crystallization of active pharmaceutical ingredients (APIs) is a crucial process in the pharmaceutical industry due to its great impact in drug effica...

Deep learning in generating radiology reports: A survey.

Substantial progress has been made towards implementing automated radiology reporting models based on deep learning (DL). This is due to the introduct...

LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening.

Comparative evaluation of virtual screening methods requires a rigorous benchmarking procedure on diverse, realistic, and unbiased data sets. Recent i...

Association Rule Learning Is an Easy and Efficient Method for Identifying Profiles of Traumas and Stressors that Predict Psychopathology in Disaster Survivors: The Example of Sri Lanka.

Research indicates that psychopathology in disaster survivors is a function of both experienced trauma and stressful life events. However, such studie...

Exploring the Role of Artificial Intelligence in an Emergency and Trauma Radiology Department.

Emergency and trauma radiologists, emergency department's physicians and nurses, researchers, departmental leaders, and health policymakers have attem...

Flaws (and quality) in research today: can artificial intelligence intervene?

The existing flaws in both conducting and reporting of research have been outlined and criticized in the past. Weak research design, poor methodology,...

Machine learning for screening prioritization in systematic reviews: comparative performance of Abstrackr and EPPI-Reviewer.

BACKGROUND: Improving the speed of systematic review (SR) development is key to supporting evidence-based medicine. Machine learning tools which semi-...

Artificial Intelligence in Dermatology: A Primer.

Artificial intelligence is becoming increasingly important in dermatology, with studies reporting accuracy matching or exceeding dermatologists for th...

Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies.

OBJECTIVE: To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep...

Reporting quality of studies using machine learning models for medical diagnosis: a systematic review.

AIMS: We conducted a systematic review assessing the reporting quality of studies validating models based on machine learning (ML) for clinical diagno...

Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness.

Machine learning, artificial intelligence, and other modern statistical methods are providing new opportunities to operationalise previously untapped ...

Identification of the Species Constituents of Maggot Populations Feeding on Decomposing Remains-Facilitation of the Determination of Post Mortem Interval and Time Since Tissue Infestation through Application of Machine Learning and Direct Analysis in Real Time-Mass Spectrometry.

The utilization of entomological specimens such as larvae (maggots) for the estimation of time since oviposition (i.e., egg laying) for post mortem in...