Identification of L5 Vertebra on Lumbar Spine Radiographs Using Deep Learning

Abstract

Abstract Background: Accurate and reliable spine numbering is critical for diagnosis, pre-procedural and preoperative planning, and treatment for spine pathology; however, it can be sometimes difficult to enumerate spine segment. Deep learning is an advanced machine-learning approach used in several medical fields. In this study, we aimed to develop a deep learning model using an object detection algorithm to identify the L5 vertebra on anteroposterior lumbar spine radiographs, and we assessed its detection accuracy. Methods: A total of 150 participants for whom both anteroposterior whole spine and lumbar spine radiographs were available were retrospectively recruited. Anteroposterior lumbar spine radiographs of 150 patients were used as input data. Of the 150 images, 105 (70%) were randomly selected as the training set, and the remaining 45 (30%) were assigned to the validation set. YOLOv5x of the YOLOv5 family model was employed to detect the L5 vertebra area. Results: The mean average precisions 0.5 and 0.75 of the trained L5 detection model were 99.2% and 96.9%, respectively. The model’s precision and recall were 95.7% and 97.8%, respectively. Of the validation data, 93.3% were detected correctly. Conclusion: Our deep learning model showed an outstanding ability to identify L5 vertebrae for spine enumeration and numbering.