ABSTRACT
PURPOSE: Endoscopic sinus surgery (ESS) is widely used to treat chronic sinusitis. However, it involves the use of surgical instruments in a narrow surgical field in close proximity to vital organs, such as the brain and eyes. Thus, an advanced level of surgical skill is expected of surgeons performing this surgery. In a previous study, endoscopic images and surgical navigation information were used to develop an automatic situation recognition method in ESS. In this study, we aimed to develop a more accurate automatic surgical situation recognition method for ESS by improving the method proposed in our previous study and adding post-processing to remove incorrect recognition. METHOD: We examined the training model parameters and the number of long short-term memory (LSTM) units, modified the input data augmentation method, and added post-processing. We also evaluated the modified method using clinical data. RESULT: The proposed improvements improved the overall scene recognition accuracy compared with the previous study. However, phase recognition did not exhibit significant improvement. In addition, the application of the one-dimensional median filter significantly reduced short-time false recognition compared with the time series results. Furthermore, post-processing was required to set constraints on the transition of the scene to further improve recognition accuracy. CONCLUSION: We suggested that the scene recognition could be improved by considering the model parameter, adding the one-dimensional filter and post-processing. However, the scene recognition accuracy remained unsatisfactory. Thus, a more accurate scene recognition and appropriate post-processing method is required.
