Analysis of hemostasis procedures through machine learning of endoscopic images towards automatic surgery

Abstract

Laparoscopic surgery reduces patient invasiveness; however, the burden on the surgeons is high because such surgery requires them to have skills higher than those for open procedures. In particular, improving the working environment of surgeons involves reducing the amount of human resources required and providing high-level medical services. The cooperation between robots and surgeons has been effective in the medical field; therefore, we focus on the automation of hemostasis procedures. An important factor in automation is target detection and the decision on the completion of the procedures. In this study, we analyzed hemostasis procedures by region detection through machine learning and developed a method of defining the termination conditions of the procedures. In hemostasis procedures, the bleeding region is coagulated by an energy device, the area of the hemostasis region increases, and the surgical procedure is continued. The method could detect the end of the procedures by monitoring the variations in the sizes of the bleeding and hemostasis regions.