AIM: To evaluate the use of deep-learning-based image reconstruction (DLIR) algorithms in dynamic contrast-enhanced computed tomography (CT) of the abdomen, and to compare the image quality and lesion conspicuity among the reconstruction strength lev...
AIM: To evaluate the suitability of a deep-learning (DL) algorithm for identifying normality as a rule-out test for fully automated diagnosis in frontal adult chest radiographs (CXR) in an active clinical pathway.
There have been substantial advances in computed tomography (CT) technology since its introduction in the 1970s. More recently, these advances have focused on image reconstruction. Deep learning reconstruction (DLR) is the latest complex reconstructi...
The use of artificial intelligence (AI) algorithms in the field of radiology is becoming more common. Several studies have demonstrated the potential utility of machine learning (ML) and deep learning (DL) techniques as aids for radiologists to solve...
AIM: To investigate the feasibility of reducing the scan time of paediatric technetium 99m (Tc) dimercaptosuccinic acid (DMSA) single-photon-emission computed tomographic (SPECT) using a deep learning (DL) method.
AIM: To assess the image quality of deep-learning image reconstruction (DLIR) of chest computed tomography (CT) images on a mediastinal window setting in comparison to an adaptive statistical iterative reconstruction (ASiR-V).
AIM: To investigate the performance of a deep-learning approach termed lesion-aware convolutional neural network (LACNN) to identify 14 different thoracic diseases on chest X-rays (CXRs).
AIM: To gather and compare related clinical studies, and to investigate the accuracy and reliability of deep learning in detecting orthopaedic fractures.