This paper introduces SKiT, a fast Key information Transformer for phase recognition of
videos. Unlike previous methods that rely on complex models to capture long-term temporal …

Deep learning (DL) is a subset of machine learning that is rapidly gaining traction in surgical
fields. Its tremendous capacity for powerful data-driven problem-solving has generated …

This is a review focused on advances and current limitations of computer vision (CV) and
how CV can help us obtain to more autonomous actions in surgery. It is a follow-up article to …

Background The growing interest in analysis of surgical video through machine learning has
led to increased research efforts; however, common methods of annotating video data are …

Surgical data science (SDS) aims to improve the quality of interventional healthcare and its
value through the capture, organization, analysis, and modeling of procedural data. As data …

The field of surgical computer vision has undergone considerable breakthroughs in recent
years with the rising popularity of deep neural network-based methods. However, standard …

Introduction Artificial intelligence and computer vision are revolutionizing the way we
perceive video analysis in minimally invasive surgery. This emerging technology has …

Surgical procedures are conducted in highly complex operating rooms (OR), comprising
different actors, devices, and interactions. To date, only medically trained human experts are …

OBJECTIVE Surgical workflow analysis involves systematically breaking down operations
into key phases and steps. Automatic analysis of this workflow has potential uses for surgical …

Surgical videos captured from microscopic or endoscopic imaging devices are rich but
complex sources of information, depicting different tools and anatomical structures utilized …