Establishment of an artificial intelligence dynamic image recognition model for lumbar endoscopic surgery and discussion on its clinical effectiveness

English Keywords:Artificial intelligence  Endoscope  Lumbar spine  Foraminoplasty  Semantic segmentation

English Abstract:

  【Abstract】 Objectives: To develop a convolutional neural network artificial intelligence(AI) identification model based on semantic segmentation to realize dynamic recognition of tissue structure during lumbar endoscopic surgery and verify its clinical value through man-machine test. Methods: We collected 60 video clips of lumbar endoscopic surgeries via the transforaminal approach from January 2020 to December 2022, and extracted 3000 high-quality images. Bones, ligaments and intervertebral discs were labeled, after which the images were divided into training set(2400), test set(300), and verification set(300). Using generative adversarial network technology to expand images from segmentation tags, a lightweight model was trained based on PaddlePaddle deep learning framework, and the optimal model was selected according to the verification set data after training. Finally, the model effect was evaluated using the test set data, and the ability of the model was verified in the man-machine test. The model′s performance was evaluated by combining precision, accuracy, recall rate, intersection over union(IoU), and dice similarity coefficient(DSC) with the confusion matrix. The receiver operating characteristic curve(ROC) and the area under the curve(AUC) were plotted. Results: The average accuracy of the model was 92.72%, the average precision was 80.59%, the average recall was 79.45%, the average IoU was 67.28%, and the average DSC was 79.97%. The AUC of intervertebral disc, bone and ligament tissues were 0.9779, 0.9626, and 0.9515, respectively. Taking the average IoU as an example, the man-machine verification results showed that senior physician(61.9%) > model(61.0%) > intermediate physician(59.5%) > junior physician(56.3%), without significant statistical differences between groups in the human-machine verification, suggesting that the model accuracy could approach the recognition ability of senior physicians. Conclusions: The AI model can label and classify the important structure under lumbar endoscopy, and its recognition ability on the anatomical structure under endoscope can reach the level of an senior physician.