To perform a systematic review (SR) and meta-analysis (MA) of outcomes of robot-assisted laparoscopic pyeloplasty (RALP) for ureteropelvic junction (UPJ) obstruction in children. A SR of the English-language literature on surgical techniques and pe...
The Journal of clinical endocrinology and metabolism
Mar 24, 2022
PURPOSE: This study investigates the efficiency of deep learning models in the automated diagnosis of Hashimoto's thyroiditis (HT) using real-world ultrasound data from ultrasound examinations by computer-assisted diagnosis (CAD) with artificial inte...
BACKGROUND: Pain costs more than $600 billion annually and affects more than 100 million Americans, but is still a poorly understood problem and one for which there is very often limited effective treatment. Electronic health records (EHRs) are the o...
BACKGROUND: Cirrhosis is the result of advanced scarring (or fibrosis) of the liver, and is often diagnosed once decompensation with associated complications has occurred. Current non-invasive tests to detect advanced liver fibrosis have limited perf...
BACKGROUND: Ultrasound is a critical non-invasive test for preoperative diagnosis of ovarian cancer. Deep learning is making advances in image-recognition tasks; therefore, we aimed to develop a deep convolutional neural network (DCNN) model that aut...
BACKGROUND: The surgery-first orthognathic approach has been applied at our institution since 2007. However, its indications remain debated. The aim of this study was to investigate the reliability of the surgery-first approach to correct facial asym...
PURPOSE: To develop and validate an artificial intelligence framework for identifying multiple retinal lesions at image level and performing an explainable macular disease diagnosis at eye level in optical coherence tomography images.
OBJECTIVES: The aim of this study was to investigate the feasibility and impact of a novel deep learning superresolution algorithm tailored to partial Fourier allowing retrospectively theoretical acquisition time reduction in 1.5 T T1-weighted gradie...
PURPOSE: The purpose of this study was to determine the efficacy of using deep learning segmentation for endotracheal tube (ETT) position on frontal chest x-rays (CXRs).
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