A multi-element identification system based on deep learning for the visual field of percutaneous endoscopic spine surgery

Abstract

Background Context:Lumbar disc herniation is a common degenerative lumbar disease with an increasing incidence.Percutaneous endoscopic lumbar discectomy can treat lumbar disc herniation safely and effectively with a minimally invasive procedure.However, it must be noted that the learning curve of this technology is steep,which means that initial learners are often not sufficiently proficient in endoscopic operations, which can easily lead to iatrogenic damage.At present, the application of computer deep learning technology to clinical diagnosis, treatment, and surgical navigation has achieved satisfactory results. Purpose:The objective of our team is to develop a multi-element identification system for the visual field of endoscopic spine surgery using deep learning algorithms and to evaluate the feasibility of this system. Study Design: Retrospective study. Patient Sample:62 patients. Outcome Measure:To determine the effectiveness of the model, the precision, recall, specificity, and mean average precision were used. Method:We established an image database by collecting surgical videos of 62 patients diagnosed with lumbar disc herniation, which was labeled by two spinal surgeons.We selected 4,840 images of the visual field of percutaneous endoscopic spine surgery (including various tissue structures and surgical instruments), divided into the training data, validation data, and test data according to 2:1:2,and trained the model based on Mask -RCNN. Result：After 108 epochs of training, the precision, recall, specificity, and mean average precision of the ResNet101 model were 76.7%、75.9%、97.9%、67.9% respectively;the precision, recall, specificity, and mean average precision of the ResNet50 model were 77.2%、76.1%、97.9%、64.8% respectively.Compared to the two convolutional neural networks, ResNet101 was found to be the most stable backbone network, with the highest convergence effect. Conclusion:Our team have developed a multi-element identification system based on Mask R-CNN for percutaneous endoscopic spine surgery ,which identifies and tracks tissues (nerve, ligamentum flavum, nucleus pulposus, etc.) and surgical instruments (endoscopic forceps, a high-speed diamond burr, etc.) in real time.It can help navigate intraoperative spinal endoscopic surgery safely in real-time.