Pedicle Screw Instrumentation of the Cervicothoracic Junction in the Sitting Position using CT-guided Navigation: Application and Technical Aspects

Background We evaluate the feasibility and potential advantages of spinal CT navigation in the placement of pedicle screws at the cervicothoracic junction in the sitting position to counteract the anatomy-related limitations of 2D fluoroscopy. Methods We retrospectively analyze the data from 15 patients who underwent CT-based navigation-guided placement of a total of 36 pedicle screws at the cervicothoracic junction in the sitting position. Results CT-based spinal navigation is a useful method in increasing accuracy of pedicle screw instrumentation in the sitting position, successfully counteracting the anatomy-related limitations of 2D fluoroscopy at the cervicothoracic junction. Conclusion CT-based navigation-guided placement of pedicle screws at the cervicothoracic junction in the sitting position proved to be an accurate, safe, and user-friendly method.

Research on Spinal Canal Generation Method based on Vertebral Foramina Inpainting of Spinal CT Images by using BEGAN

Spinal surgery is of high risk due to the possibility of neurologic damage, which may cause life-threatening sequelae. Although the emerging robotic-assisted spinal surgery provides better accuracy compared with traditional surgery, the construction of boundary constraints around the spinal canal for safety in surgery is still required. The establishment of a three-dimensional (3D) model of the spinal canal during preoperative preparation can facilitate the generation of surgical boundary constraints. This article presents a novel framework for spinal canal generation based on spinal CT image inpainting by using the boundary equilibrium generative adversarial network (BEGAN). First, U-net is used to simplify the image features and then ResNet50 is applied to classify the vertebral foramen features and mark the area to be restored. Finally, BEGAN generates the target features to complete the vertebral foramina inpainting for the generation of the spinal canal. The experimental results show that the average accuracies (Mean Intersection over Union) of the vertebral foramina and spine inpainting are 0.9396 and 0.9332, respectively, and the accuracy of image inpainting decreases with increase in the inpainting area. The proposed method can accurately generate the vertebral contours and complete the 3D reconstruction of the spinal canal.

Vacuum phenomena on spinal CT scans and its association with intervertebral disc disease in dogs

Vacuum phenomenon (VP) refers to the presence of gas in joint spaces, including those in the spinal column. The phenomenon is associated with progressive and chronic diseases, such as intervertebral disc disease (IVDD). We reviewed 55 canine spinal CT images for the presence of VP, and studied its association with IVDD to determine the association between the VP and the clinical signs exhibited by the patients at the time of exam. All images were obtained between January 2016 and July 2018, at the Diagnostic Imaging Service of a Veterinary Hospital. Image consistent with VP was observed in 7 (12.7%) of the 55 cases. Of these 7 animals, pain and paresis was reported in 3, pain with plegia in 1, and plegia without pain in 3 dogs. One dog did not have a confirmed diagnosis of IVDD. Further studies with larger samples are still needed to confirm the relevance of this phenomenon in the diagnosis of IVDD in dogs.

Application of deep learning for clinical predictive modeling: An artificial intelligence recognition in spinal metastases.

e18050 Background: Spinal metastases are very common outcomes within solid malignant tumors, which could lead to various skeletal related events (SREs). The accurate and timely diagnosis is the key to improve prognosis. Recently, artificial intelligence(AI) has assisted doctors in many ways by different AI technologies. In this study, we applicated a deep learning model to classify and locate the metastatic lesions on spinal CT images. Methods: We set up a dataset consisting of 800 patients’ spinal CT images, which contained over 300,000 CT slices. And we built a multi-label classification and vertebrae segmentation model to recognize the metastatic lesions on spinal CT images. Then we trained and tested this model within our dataset, using a data augmentation by random flips and random rotations. Sensitivity and specificity were used to evaluate the performance of the model. Results: Our model showed that the diagnostic utilities of normal lesions were: sensitivity 81.7% and specificity 92%; while the diagnostic utilities of metastatic lesions were: sensitivity 84.7% and specificity 84.5%. Conclusions: Our model can effectively and accurately discriminate spinal metastases on spinal CT images. [Table: see text]