Executive summary Traumatic brain injury (TBI) has the highest incidence of all common
neurological disorders, and poses a substantial public health burden. TBI is increasingly …

Purpose To assess generalizability of published deep learning (DL) algorithms for radiologic
diagnosis. Materials and Methods In this systematic review, the PubMed database was …

Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …

The diagnosis and treatment of patients with cancer requires access to imaging to ensure
accurate management decisions and optimal outcomes. Our global assessment of imaging …

Artificial intelligence technology is a rapidly expanding field with many applications in acute
stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute …

Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT
Hemorrhage Challenge | Radiology: Artificial Intelligence RSNA "skipMainNavigation" …

Background Machine learning (ML), a type of artificial intelligence (AI) technology that uses
a data-driven approach for pattern recognition, has been shown to be beneficial for many …

The advent of artificial intelligence (AI) in medicine has transformed various medical
specialties, including orthodontics. AI has shown promising results in enhancing the …

Acute Intracranial hemorrhage (ICH) is a life-threatening disease that requires emergency
medical attention, which is routinely diagnosed using non-contrast head CT imaging. The …

Purpose Building accurate and robust artificial intelligence systems for medical image
assessment requires the creation of large sets of annotated training examples. However …