Surveillance

The feasibility to use artificial intelligence to aid detecting focal liver lesions in real-time ultrasound: a preliminary study based on videos.

Despite the wide availability of ultrasound machines for hepatocellular carcinoma surveillance, an inadequate number of expert radiologists performing ultrasounds in remote areas remains a primary barrier for surveillance. We demonstrated feasibility of artificial intelligence (AI) to aid in the detection of focal liver lesions (FLLs) during ultrasound. An AI system for FLL detection in ultrasound...

Deep Learning for Person Re-Identification: A Survey and Outlook.

Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. By dissecting the involved components in developing a person Re-ID system, we categorize it into the cl...

Reporting details of neuroimaging studies on individual traits prediction: A literature survey.

Using machine-learning tools to predict individual phenotypes from neuroimaging data is one of the most promising and hence dynamic fields in systems ...

Predictive models for clinical decision making: Deep dives in practical machine learning.

The deployment of machine learning for tasks relevant to complementing standard of care and advancing tools for precision health has gained much atten...

Settings matter: a scoping review on parameters in robot-assisted gait therapy identifies the importance of reporting standards.

BACKGROUND: Lokomat therapy for gait rehabilitation has become increasingly popular. Most evidence suggests that Lokomat therapy is equally effective ...

Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review.

COVID-19 is a disease caused by SARS-CoV-2 and has been declared a worldwide pandemic by the World Health Organization due to its rapid spread. Since ...

Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology.

Nutritional epidemiology employs observational data to discover associations between diet and disease risk. However, existing analytic methods of diet...

Proposing Causal Sequence of Death by Neural Machine Translation in Public Health Informatics.

Each year there are nearly 57 million deaths worldwide, with over 2.7 million in the United States. Timely, accurate and complete death reporting is c...

Deep learning time series prediction models in surveillance data of hepatitis incidence in China.

BACKGROUND: Precise incidence prediction of Hepatitis infectious disease is critical for early prevention and better government strategic planning. In...

Deep Learning Prediction of Ovarian Malignancy at US Compared with O-RADS and Expert Assessment.

Background Deep learning (DL) algorithms could improve the classification of ovarian tumors assessed with multimodal US. Purpose To develop DL algorit...

A Real-Time Crowd Monitoring and Management System for Social Distance Classification and Healthcare Using Deep Learning.

Coronavirus born COVID-19 disease has spread its roots in the whole world. It is primarily spread by physical contact. As a preventive measure, proper...

BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks.

Estimating the parameters of mathematical models is a common problem in almost all branches of science. However, this problem can prove notably diffic...

Vision Transformer and Deep Sequence Learning for Human Activity Recognition in Surveillance Videos.

Human Activity Recognition is an active research area with several Convolutional Neural Network (CNN) based features extraction and classification met...

Machine learning approach to identify adverse events in scientific biomedical literature.

Monitoring the occurrence of adverse events in the scientific literature is a mandatory process in drug marketing surveillance. This is a very time-co...

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models.

Coronavirus disease 2019 (COVID-19) is a novel disease that affects healthcare on a global scale and cannot be ignored because of its high fatality ra...

Intelligent Wearable Devices Enabled Automatic Vehicle Detection and Tracking System with Video-Enabled UAV Networks Using Deep Convolutional Neural Network and IoT Surveillance.

The discipline of computer vision is becoming more popular as a research subject. In a surveillance-based computer vision application, item identifica...

Deep learning approach identified a gene signature predictive of the severity of renal damage caused by chronic cadmium accumulation.

Epidemiology studies have indicated that environmental cadmium exposure, even at low levels, will result in chronic cadmium accumulation in the kidney...

Epidemiological predictive modeling: lessons learned from the Kuopio ischemic heart disease risk factor study.

PURPOSE: The use of predictive models in epidemiology is relatively narrow as most of the studies report results of traditional statistical models suc...

Deep Learning Based SWIR Object Detection in Long-Range Surveillance Systems: An Automated Cross-Spectral Approach.

SWIR imaging bears considerable advantages over visible-light (color) and thermal images in certain challenging propagation conditions. Thus, the SWIR...

What Influences the Way Radiologists Express Themselves in Their Reports? A Quantitative Assessment Using Natural Language Processing.

Although using standardized reports is encouraged, most emergency radiological reports in France remain in free-text format that can be mined with nat...