RESEARCH QUESTION: Can models based on artificial intelligence predict embryonic ploidy status or implantation potential of euploid transferred embryos? Can the addition of clinical features into time-lapse monitoring (TLM) parameters as input data i...
The goal of this study was to quantify and compare prospective self-reported intraoperative workload and teamwork during robot-assisted radical prostatectomy (RARP) for multi-port da Vinci Xi (MP) and single-port da Vinci SP (SP) robots. The self-rep...
PURPOSE: To enable a fast and automatic deep learning-based QSM reconstruction of tissues with diverse chemical shifts, relevant to most regions outside the brain.
BACKGROUND: Public community engagement is crucial for mosquito surveillance programs. To support community participation, one of the approaches is assisting the public in recognizing the mosquitoes that carry pathogens. Therefore, this study aims to...
PURPOSE: To investigate whether the image quality of a specific deep learning-based synthetic CT (sCT) of the cervical spine is noninferior to conventional CT.
The rapid spread of COVID-19 has prompted a surge in the adoption of technology, highlighting a number of potential applications for Socially Assistive Robots (SARs). Our entire healthcare system has been under unprecedented strain, and going forward...
We report the use of robot assistance for computed tomography-guided celiac plexus neurolysis for the first time. Four patients of upper abdominal cancer with intractable pain despite opioids were positioned prone on the PET-computed tomography sca...
BACKGROUND: Extended postoperative hospital stays are associated with numerous clinical risks and increased economic cost. Accurate preoperative prediction of extended length of stay (LOS) can facilitate targeted interventions to mitigate clinical ha...
Since segmentation labeling is usually time-consuming and annotating medical images requires professional expertise, it is laborious to obtain a large-scale, high-quality annotated segmentation dataset. We propose a novel weakly- and semi-supervised ...
Disentangled representation learning has been proposed as an approach to learning general representations even in the absence of, or with limited, supervision. A good general representation can be fine-tuned for new target tasks using modest amounts ...
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