Nerve recognition in percutaneous transforaminal endoscopic discectomy using convolutional neural network.

Abstract

Percutaneous transforaminal endoscopic discectomy (PTED) is one of the most common minimally invasive surgery methods used in clinic in recent years. In this study, we developed a computer-aided detection system (CADS) based on convolutional neural network (CNN) to automatically recognize nerve and dura mater images under PTED surgery. We collected surgical videos from 65 patients with lumbar disc herniation who underwent PTED; we then converted the videos into images, and randomly divided some images into a training dataset, a validation dataset, test dataset. The training dataset and validation dataset were composed of 10454 images containing nerve and dura mater from 50 randomly selected patients; test dataset contained 12000 images from the remaining 15 patients. The results showed that sensitivity, specificity, and accuracy reached 90.90%, 93.68%, and 92.29%, respectively. CADS could recognize the nerve and dura mater with no significant difference (P>0.05) between each patient in test dataset. In comparison with clinicians of different levels, the performance of CADS was lower than that of a spinal endoscopist, but significantly higher than that of general surgeons. With the assistance of CADS, the performance of the general surgeons approached that of the spinal endoscopist. CNN can recognize well nerve and dura mater images in PTED surgery, and can help general surgeons to improve their ability to recognize tissues during the operation.