Training the modern ophthalmic surgeon is a challenging process. Microsurgical education can benefit from innovative methods to practice surgery in low-risk simulations, assess and refine skills in the operating room through video content analytics, ...
Journal of cardiovascular computed tomography
Aug 10, 2020
BACKGROUND: Quantitative coronary plaque parameters are increasingly being utilized as surrogate endpoints of pharmaceutical trials. However, little is known whether differences in segmentation significantly alter parameter values.
OBJECTIVE: Laparoscopic box simulators provide surgical residents a cost-effective and accessible learning tool to practice basic laparoscopic skills. Despite effective, high-fidelity simulators used in robotic surgery training, a similar low-fidelit...
Background There is great interest in developing artificial intelligence (AI)-based computer-aided detection (CAD) systems for use in screening mammography. Comparative performance benchmarks from true screening cohorts are needed. Purpose To determi...
PURPOSE: To (1) develop a deep learning system (DLS) using a deep convolutional neural network (DCNN) for identification of pneumothorax, (2) compare its performance to first-year radiology residents, and (3) evaluate the ability of a DLS to augment ...
BACKGROUND: Current evaluation methods for robotic-assisted surgery (ARCS or GEARS) are limited to 5-point Likert scales which are inherently time-consuming and require a degree of subjective scoring. In this study, we demonstrate a method to break d...
International journal of surgery (London, England)
May 12, 2020
BACKGROUND: Identifying laparoscopic surgical videos using artificial intelligence (AI) facilitates the automation of several currently time-consuming manual processes, including video analysis, indexing, and video-based skill assessment. This study ...
• The studies on AI reading of screening mammograms have methodological limitations that undermine the conclusion that AI could do better than radiologists. • These studies do not informon numbers of extra breast cancers found by AI that could repres...
The Journal of investigative dermatology
Mar 31, 2020
Although deep learning algorithms have demonstrated expert-level performance, previous efforts were mostly binary classifications of limited disorders. We trained an algorithm with 220,680 images of 174 disorders and validated it using Edinburgh (1,3...