BACKGROUND: Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and te...
The advent of precision medicine, increasing clinical needs, and imaging availability among many other factors in the prostate cancer diagnostic pathway has engendered the utilization of artificial intelligence (AI). AI carries a vast number of poten...
INTRODUCTION: Robotic inguinal hernia repair (RIHR) is becoming increasingly common and is the minimally invasive alternative to laparoscopic inguinal hernia repair (LIHR). Thus far, there is little data directly comparing LIHR and RIHR. The purpose ...
RESEARCH QUESTION: Can a novel deep learning-based follicle volume biomarker using three-dimensional ultrasound (3D-US) be established to aid in the assessment of oocyte maturity, timing of HCG administration and the individual prediction of ovarian ...
BACKGROUND AND AIMS: Chronic atrophic gastritis (CAG) is a precancerous disease that often leads to the development of gastric cancer (GC) and is positively correlated with GC morbidity. However, the sensitivity of the endoscopic diagnosis of CAG is ...
BACKGROUND: Extended pelvic nodal dissection (ePLND) represents the gold standard for nodal staging in prostate cancer (PCa). Prostate-specific membrane antigen (PSMA) radioguided surgery (RGS) could identify lymph node invasion (LNI) during robot-as...
OBJECTIVE: To evaluate the clinical effectiveness of micro-hand robot-assisted cholecystectomy (MRC) by comparing the clinical outcomes of patients with benign gallbladder disease treated with micro-hand or da Vinci robot-assisted cholecystectomy (DR...
Background and Objectives: Although reducing the radiation dose level is important during diagnostic computed tomography (CT) applications, effective image quality enhancement strategies are crucial to compensate for the degradation that is caused by...
PURPOSE: This paper aims to develop a successful deep learning model with data augmentation technique to discover the clinical uniqueness of chest X-ray imaging features of coal workers' pneumoconiosis (CWP).