Background: Abdominal cavity irrigation is a more minimally invasive surgery than that using a gas. Minimally invasive surgery improves the quality of life of patients; however, it demands higher skills from the doctors. Therefore, the study aimed to reduce the burden by assisting and automating the hemostatic procedure a highly frequent procedure by taking advantage of the clearness of the endoscopic images and continuous bleeding point observations in the liquid. We aimed to construct a method for detecting organs, bleeding sites, and hemostasis regions.Methods: We developed a method to perform real-time detection based on machine learning using laparoscopic videos. Our training dataset was prepared from three experiments in pigs. Linear support vector machine was applied using new color feature descriptors. In the verification of the accuracy of the classifier, we performed five-part cross-validation. Classification processing time was measured to verify the real-time property. Furthermore, we visualized the time series class change of the surgical field during the hemostatic procedure.Results: The accuracy of our classifier was 98.3% and the processing cost to perform real-time was enough. Furthermore, it was conceivable to quantitatively indicate the completion of the hemostatic procedure based on the changes in the bleeding region by ablation and the hemostasis regions by tissue coagulation.Conclusions: The organs, bleeding sites, and hemostasis regions classification was useful for assisting and automating the hemostatic procedure in the liquid. Our method can be adapted to more hemostatic procedures.
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.
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