Objective Maintaining the occipito-axial (O–C2) angle following occipitocervical fusion is crucial to prevent postoperative complications. Although automated O–C2 measurement has been reported, practical methods that provide rapid results for routine practice remain limited. This study aimed to develop a deep learning model using the YOLO (You Only Look Once) object detection algorithm to automatically identify anatomical landmarks and rapidly calculate the O–C2 angle.
Methods A retrospective analysis was conducted using cervical spine radiographs from 2 independent facilities. The internal dataset comprised 574 lateral cervical radiographs from 271 patients for model development, while the external validation dataset included 100 radiographs from 100 patients. Model performance was evaluated against manual measurements by 3 expert raters.
Results The model demonstrated excellent detection performance, achieving perfect metrics for the hard palate (F1 score: 1.00) and high performance for the occipital bone (F1 score: 0.97), anteroinferior corner of C2 (F1 score: 0.99), and posteroinferior corner of C2 (F1 score: 0.99). For O–C2 angle estimation, the mean absolute error was 2.35° and root mean squared error was 2.98°, with an accuracy of 94.7% for determining the presence or absence of the O–C2 angle (i.e., whether all 4 anatomical landmarks were simultaneously detected). Bland-Altman analysis revealed minimal bias (0.57°; 95% confidence interval, -0.06° to 1.12°) with limits of agreement from -5.19° to 6.33°. Inference time was approximately 0.14 s per image.
Conclusion Our deep learning model enables rapid and accurate O–C2 angle measurement on lateral cervical radiographs, demonstrating performance comparable to expert raters and potential clinical utility.
Objective Virtual and augmented reality have enjoyed increased attention in spine surgery. Preoperative planning, pedicle screw placement, and surgical training are among the most studied use cases. Identifying osseous structures is a key aspect of navigating a 3-dimensional virtual reconstruction. To automate the otherwise time-consuming process of labeling vertebrae on each slice individually, we propose a fully automated pipeline that automates segmentation on computed tomography (CT) and which can form the basis for further virtual or augmented reality application and radiomic analysis.
Methods Based on a large public dataset of annotated vertebral CT scans, we first trained a YOLOv8m (You-Only-Look-Once algorithm, Version 8 and size medium) to detect each vertebra individually. On the then cropped images, a 2D-U-Net was developed and externally validated on 2 different public datasets.
Results Two hundred fourteen CT scans (cervical, thoracic, or lumbar spine) were used for model training, and 40 scans were used for external validation. Vertebra recognition achieved a mAP50 (mean average precision with Jaccard threshold of 0.5) of over 0.84, and the segmentation algorithm attained a mean Dice score of 0.75 ± 0.14 at internal, 0.77 ± 0.12 and 0.82 ± 0.14 at external validation, respectively.
Conclusion We propose a 2-stage approach consisting of single vertebra labeling by an object detection algorithm followed by semantic segmentation. In our externally validated pilot study, we demonstrate robust performance for our object detection network in identifying individual vertebrae, as well as for our segmentation model in precisely delineating the bony structures.
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