The performance and clinical implications of the deep learning aided algorithm using electrocardiogram of heart failure (HF) with reduced ejection fraction (DeepECG-HFrEF) were evaluated in patients with acute HF. The DeepECG-HFrEF algorithm was trai...
OBJECTIVE: 3D reconstruction of lumbar intervertebral foramen (LIVF) has been beneficial in evaluating surgical trajectory. Still, the current methods of reconstructing the 3D LIVF model are mainly based on manual segmentation, which is laborious and...
European journal of cancer (Oxford, England : 1990)
Aug 16, 2022
BACKGROUND: The need for developing new biomarkers is increasing with the emergence of many targeted therapies. Artificial Intelligence (AI) algorithms have shown great promise in the medical imaging field to build predictive models. We developed a p...
Deep inspiration breath-hold (DIBH) is widely used to reduce the cardiac dose in left-sided breast cancer radiotherapy. This study aimed to develop a deep learning chest X-ray model for cardiac dose prediction to select patients with a potentially hi...
AJR. American journal of roentgenology
Aug 10, 2022
Coronary CTA with hybrid iterative reconstruction (HIR) is prone to false-positive results for in-stent restenosis due to stent-related blooming artifact. The purpose of this study is to assess the impact of deep learning reconstruction (DLR), subt...
Background Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate approved for use in human epidermal growth factor receptor 2 (HER2)-positive breast cancer. Case reports have suggested an association between T-DM1 and portal hypertension. Purpo...
Journal of orthopaedic surgery and research
Aug 8, 2022
OBJECTIVE: To determine the rates and risk factors of pedicle screw placement accuracy and the proximal facet joint violation (FJV) using TINAVI robot-assisted technique.
Endocrinology and metabolism (Seoul, Korea)
Aug 5, 2022
BACKGRUOUND: Since image-based fracture prediction models using deep learning are lacking, we aimed to develop an X-ray-based fracture prediction model using deep learning with longitudinal data.
PURPOSE: The purpose of this study was to assess the efficiency, safety, and accuracy of cannulated screw fixation using a robot-assisted method compared with a traditional percutaneous freehand method.
PURPOSE: To compare image quality and interobserver agreement in evaluations of neuroforaminal stenosis between 1.5T cervical spine magnetic resonance imaging (MRI) with deep learning reconstruction (DLR) and 3T MRI without DLR.
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