We developed a machine learning algorithm to analyze trauma-related data and predict the mortality and chronic care needs of patients with trauma. We recruited admitted patients with trauma during 2015 and 2016 and collected their clinical data. Th...
OBJECTIVE: To compare the characteristics of conventional laparoscopic pyeloplasty (LP) and robotic-assisted laparoscopic pyeloplasty (RALP) in infants and young children with ureteropelvic junction obstruction (UPJO).
BACKGROUND: There is no simple model to screen for Alzheimer's disease, partly because the diagnosis of Alzheimer's disease itself is complex-typically involving expensive and sometimes invasive tests not commonly available outside highly specialised...
PURPOSE: We evaluated the diagnostic performance of a clinically available deep learning-based computer-assisted diagnosis software for detecting unruptured aneurysms (UANs) using magnetic resonance angiography and assessed the functionality of the c...
OBJECTIVE: Patients with indeterminate pulmonary nodules (IPN) with an intermediate to a high probability of lung cancer generally undergo invasive diagnostic procedures. Chest computed tomography image and clinical data have been in estimating the p...
We propose a deep learning-assisted overscan decision algorithm in chest low-dose computed tomography (LDCT) applicable to the lung cancer screening. The algorithm reflects the radiologists' subjective evaluation criteria according to the Korea insti...
Journal of magnetic resonance imaging : JMRI
Sep 28, 2022
BACKGROUND: An inherently poor signal-to-noise ratio (SNR) causes inaccuracy and less precision in cerebral blood flow (CBF) and arterial transit time (ATT) when using arterial spin labeling (ASL). Deep neural network (DNN)-based parameter estimation...
PURPOSE: To review a series of adolescent patients with anterior cruciate ligament (ACL) injuries surgically treated with robot-assisted all-epiphyseal ACL reconstruction (ACLR), and to compare with the traditional freehand group.
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