Detection of unsafe action from laparoscopic cholecystectomy video

Abstract

Wellness and healthcare are central to the lives of all people, young or old, healthy or ill, rich or poor. New computing and behavioral research can lead to transformative changes in the cost-effective delivery of quality and personalized healthcare. Also beyond the daily practice of healthcare and wellbeing, basic information technology research can provide the foundations for new directions in the clinical sciences via tools and analyses that identify subtle but important causal signals in the fusing of clinical, behavioral, environmental and genetic data. In this paper we describe a system that analyzes images from the laparoscopic videos. It indicates the possibility of an injury to the cystic artery by automatically detecting the proximity of the surgical instruments with respect to the cystic artery. The system uses machine learning algorithm to classify images and warn surgeons against probable unsafe actions.