BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big
data applications for mental health, highlighting current research and applications in …

Rationale and Objectives The automated segmentation of organs and tissues throughout the
body using computed tomography and magnetic resonance imaging has been rapidly …

Application of a deep learning algorithm for detection and visualization of hip fractures on plain
pelvic radiographs | European Radiology Skip to main content SpringerLink Account Menu …

Multi-modal biomarkers (eg, imaging, blood-based, physiological) of unique traumatic brain
injury (TBI) endophenotypes are necessary to guide the development of personalized and …

Background White matter hyperintensities (WMH), of presumed vascular origin, are visible
and quantifiable neuroradiological markers of brain parenchymal change. These changes …

Despite increasing public awareness and a growing body of literature on the subject of
concussion, or mild traumatic brain injury, an urgent need still exists for reliable diagnostic …

Background: Traumatic brain injury (TBI) is a leading cause of death and disability
worldwide. The use of machine learning (ML) has emerged as a key advancement in TBI …

Background With recent transformations in medical education, the integration of technology
to improve medical students' abilities has become feasible. Artificial intelligence (AI) has …

Recently, the application of artificial intelligence (AI) in medical imaging (including nuclear
medicine imaging) has rapidly developed. Most AI applications in nuclear medicine imaging …

BACKGROUND AND PURPOSE: Most brain lesions are characterized by hyperintense
signal on FLAIR. We sought to develop an automated deep learning–based method for …