Method for end time prediction of brain tumor resections using analysis of surgical navigation information and tumor size characteristics

The rapid development in science and technology in recent years has led to corresponding developments in medical technology. At the same time, however, surgical treatments and procedures are complicated by a variety of techniques and applications of medical devices, which has led to an increase in the demands on surgical staff. To address this problem, surgical workflow analyses have been carried out to ensure the reliability of surgical techniques by visualizing and analyzing complex surgery processes. In previous studies, we visualized the removal progress of malignant brain tumors during MRI-guided navigation surgery and developed a technique to predict the time required for tumor removal using analysis of surgical navigation information. In this paper, we introduce a new method and the results of the end time prediction of tumor removal using both intraoperative performance measurements with surgical navigation data and the estimated mean incremental speed of progress using regression analysis of 20 brain tumor resection cases based on the relationship between tumor size (volume or surface direction) and removal time. The results show that the accuracy was significantly improved from the estimation based on regression analysis by adding the intraoperative progress analysis.