Latest AI and machine learning research in radiology for healthcare professionals.
Among numerous medical imaging modalities, ultrasound imaging is one of the most commonly used diagnostic methods in clinical practice. However, ultrasound diagnosis heavily relies on physician experience, and diagnostic results often lack reproducibility. In recent years, with the rapid development of artificial intelligence technology, which provides new impetus for the automated medical ultraso...
OBJECTIVE: To develop a robust and compact deep learning model for automated knee cartilage segmentation on point-of-care ultrasound (POCUS) devices. METHODS: We propose MonoUNet, a novel, highly compact segmentation model consisting of (i) an aggressively reduced U-Net backbone, (ii) a trainable monogenic block that extracts multi-scale local phase features from the input, and (iii) a gating mech...
BACKGROUND: Stroke causes neurological impairment through local plasticity mechanisms, such as synaptic sprouting, and large-scale network reorganizat...
PURPOSE OF REVIEW: To review and summarize the current literature on recent advances in corneal topography and anterior segment tomography, highlighti...
INTRODUCTION AND HYPOTHESIS: We developed a dual-task deep-learning model, termed FD-Net, which utilizes fused two-dimensional (2D) and three-dimensio...
Elbow injury rates are markedly higher among collegiate athletes than in the general population; however, this elevated incidence is largely driven by...
OBJECTIVE: To compare diagnostic performance of four radiomics-based machine learning models for detecting Modic type 1-changes of the lumbar spine in...
Coronary artery disease assessment has long focused on stenosis severity, yet luminal narrowing alone fails to capture ischemic burden, plaque vulnera...
The scarcity of semantically labelled data presents major challenges for medical image segmentation using deep learning models, and the "black-box" na...
Optical coherence tomography (OCT) is extremely useful in the screening and detection of oral cancers. But various challenges, such as its subjectivit...
Musculoskeletal radiology has transitioned to "musculoskeletal imaging and intervention," encompassing both traditional diagnostic roles and specializ...
The study aimed to compare image quality between hybrid iterative reconstruction and deep learning image reconstruction (DLIR) in low dose contrast en...
PURPOSE: To systematically investigate the diagnostic performance of magnetic resonance imaging (MRI)-based radiomics and deep learning (DL) models fo...
INTRODUCTION: Idiopathic normal pressure hydrocephalus (iNPH) is frequently underdiagnosed due to non-specific symptoms and the risks of invasive test...
Thrombosis is a multifaceted pathological process involving intravascular clot formation that drives a range of cardiovascular and cerebrovascular dis...
BACKGROUND: Less women participate in Alzheimer Disease (AD) trials compared to their estimated representation in the global dementia population. OBJE...
BACKGROUND: Accurate segmentation is central for the diagnosis and treatment of gliomas. Although manual segmentation remains the clinical standard, i...
BACKGROUND: Micro-ultrasound (micro-US) is a clinically available novel high-resolution imaging technology for guiding prostate biopsies. However, cli...
BACKGROUND: Frailty is a risk factor for adverse outcomes in patients undergoing mitral valve transcatheter edge-to-edge repair (M-TEER) and is interr...
BACKGROUND: The impact of deep learning (DL) reconstruction and segmentation on MRI radiomics stability has not been fully assessed. PURPOSE: To inves...