European journal of physical and rehabilitation medicine
29265791
BACKGROUND: Active performance is crucial for motor learning, and, together with motivation, is believed to be associated with a better rehabilitation outcome. Virtual reality (VR) is an innovative approach to engage and motivate patients during trai...
OBJECTIVE: To train a generic deep learning software (DLS) to classify breast cancer on ultrasound images and to compare its performance to human readers with variable breast imaging experience.
Screening references is a time-consuming step necessary for systematic reviews and guideline development. Previous studies have shown that human effort can be reduced by using machine learning software to prioritise large reference collections such t...
OBJECTIVE: High breast density is a risk factor for breast cancer. The aim of this study was to develop a deep convolutional neural network (dCNN) for the automatic classification of breast density based on the mammographic appearance of the tissue a...
PURPOSE: To evaluate the opinion and assessment of radiologists, surgeons and medical students on a number of important topics regarding the future of radiology, such as artificial intelligence (AI), turf battles, teleradiology and 3D-printing.
Machine Learning (ML) can improve the analysis of complex and interrelated factors that place adherent people at risk of viral rebound. Our aim was to build ML model to predict RNA viral rebound from medication adherence and clinical data. Patients w...
BACKGROUND: Prolonged forensic psychiatric hospitalizations have raised ethical, economic, and clinical concerns. Due to the confounded nature of factors affecting length of stay of psychiatric offender patients, prior research has called for the app...
Zeitschrift fur Gerontologie und Geriatrie
33005983
BACKGROUND: Residential old age institutions are currently undergoing a digital transformation process, which is characterized by an orientation towards increasing digitalization of work processes as well as the institutional infrastructure. But what...
BACKGROUND: It is unclear whether data-driven machine learning models, which are trained on large epidemiological cohorts, may improve prediction of comorbidities in people living with human immunodeficiency virus (HIV).