We present a comprehensive analysis of the submissions to the first edition of the Endoscopy Artefact Detection challenge (EAD). Using crowd-sourcing, this initiative is a step towards understanding the limitations of existing state-of-the-art comput...
Journal of neuroengineering and rehabilitation
Feb 17, 2020
BACKGROUND: The worldwide population of older adults will soon exceed the capacity of assisted living facilities. Accordingly, we aim to understand whether appropriately designed robots could help older adults stay active at home.
BACKGROUND: Epicardial adipose tissue (EAT) volume (cm) and attenuation (Hounsfield units) may predict major adverse cardiovascular events (MACE). We aimed to evaluate the prognostic value of fully automated deep learning-based EAT volume and attenua...
Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients experience swift recovery, others show relapsing-remitting or chronic courses. Predicting individual cl...
Vitamin D insufficiency has been associated with faster progression of atherosclerosis and increased cardiovascular disease risk, but limited data are available in HIV-infected people. So, we examined potential correlation between vitamin D status a...
OBJECTIVES: To take advantage of the deep learning algorithms to detect and calculate clot burden of acute pulmonary embolism (APE) on computed tomographic pulmonary angiography (CTPA).
Circulation. Arrhythmia and electrophysiology
Feb 16, 2020
BACKGROUND: Deep learning algorithms derived in homogeneous populations may be poorly generalizable and have the potential to reflect, perpetuate, and even exacerbate racial/ethnic disparities in health and health care. In this study, we aimed to (1)...
PURPOSE: To develop and validate a deep learning model for the automatic segmentation of geographic atrophy (GA) using color fundus images (CFIs) and its application to study the growth rate of GA.
Clinical nutrition (Edinburgh, Scotland)
Feb 14, 2020
BACKGROUND: There is a growing interest in fast and reliable assessment of abdominal visceral adipose tissue (VAT) volume for risk stratification of metabolic disorders. However, imaging based measurement of VAT is costly and limited by scanner avail...
OBJECTIVES: To investigate whether a deep learning model can predict the bone mineral density (BMD) of lumbar vertebrae from unenhanced abdominal computed tomography (CT) images.
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.