Latest AI and machine learning research in surveillance for healthcare professionals.
This work proposes a new system capable of real-time ship instance segmentation during maritime surveillance missions by unmanned aerial vehicles using an onboard standard RGB camera. The implementation requires two stages: an instance segmentation network able to produce fast and reliable preliminary segmentation results and a post-processing 3D fully connected Conditional Random Field, which sig...
Automated interictal epileptiform discharge (IED) detection has been widely studied, with machine learning methods at the forefront in recent years. As computational resources become more accessible, researchers have applied deep learning (DL) to IED detection with promising results. This systematic review aims to provide an overview of the current DL approaches to automated IED detection from sca...
Combining deep learning image analysis methods and large-scale imaging datasets offers many opportunities to neuroscience imaging and epidemiology. Ho...
Monitoring the status of culture fish is an essential task for precision aquaculture using a smart underwater imaging device as a non-intrusive way of...
BACKGROUND: Measurement of care quality and safety mainly relies on abstracted administrative data. However, it is well studied that administrative da...
Survival analysis deals with the expected duration of time until one or more events of interest occur. Time to the event of interest may be unobserved...
An accurate estimation of glomerular filtration rate (GFR) is clinically crucial for kidney disease diagnosis and predicting the prognosis of chronic ...
Recognition of individual pigs is critical to the monitoring of pig body size and physiological health status in large-scale pig farms. In this study,...
In low earth orbit (LEO) satellite-based applications (e.g., remote sensing and surveillance), it is important to efficiently transmit collected data ...
Robot-assisted anti-reflux surgery (RA-ARS) is increasingly being used to treat refractory gastro-oesophageal reflux disease. The IDEAL (Idea, Develop...
OBJECTIVES: The aim of this study was to evaluate the quality of reporting of randomised controlled trials (RCTs) of artificial intelligence (AI) in h...
This article summarises a recent virtual meeting organised by the Oxford University Clinical Research Unit in Vietnam on the topic of climate change a...
In the recent past, a huge number of cameras have been placed in a variety of public and private areas for the purposes of surveillance, the monitorin...
BACKGROUND: Natural language processing (NLP) is a discipline of machine learning concerned with the analysis of language and text. Although NLP has b...
OBJECTIVES: To assess the performance of DeepSurv, a deep learning-based model in the survival prediction of laryngeal squamous cell carcinoma (LSCC) ...
Machine learning (ML) algorithms are becoming increasingly pervasive in the domains of medical diagnostics and prognostication, afforded by complex de...
Background Models predicting atrial fibrillation (AF) risk, such as Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF), have ...
OBJECTIVES: To determine the reproducibility and replicability of studies that develop and validate segmentation methods for brain tumours on MRI and ...
UNLABELLED: The evolution of the Exposome concept revolutionised the research in exposure assessment and epidemiology by introducing the need for a mo...
Policing efforts to thwart crime typically rely on criminal infraction reports, which implicitly manifest a complex relationship between crime, polici...