ABSTRACT
Background Degenerative spinal conditions (DSCs) involve a diverse set of pathologies that significantly impact health and quality of life, affecting many individuals at least once during their lifetime. Treatment approaches are varied and complex, reflecting the intricacy of spinal anatomy and kinetics. Diagnosis and management pose challenges, with the accurate detection of lesions further complicated by age-related degeneration and surgical implants. Technological advancements, particularly in artificial intelligence (AI) and deep learning, have demonstrated the potential to enhance detection of spinal lesions. Despite challenges in dataset creation and integration into clinical settings, further research holds promise for improved patient outcomes. Methods This study aimed to develop a DSC detection and classification model using a Kaggle dataset of 967 spinal X-ray images at the Department of Neurosurgery of Arrowhead Regional Medical Center, Colton, California, USA. Our entire workflow, including data preprocessing, training, validation, and testing, was performed by utilizing an online-cloud based AI platform. The model's performance was evaluated based on its ability to accurately classify certain DSCs (osteophytes, spinal implants, and foraminal stenosis) and distinguish these from normal X-rays. Evaluation metrics, including accuracy, precision, recall, and confusion matrix, were calculated. Results The model achieved an average precision of 0.88, with precision and recall values of 87% and 83.3%, respectively, indicating its high accuracy in classifying DSCs and distinguishing these from normal cases. Sensitivity and specificity values were calculated as 94.12% and 96.68%, respectively. The overall accuracy of the model was calculated to be 89%. Conclusion These findings indicate the utility of deep learning algorithms in enhancing early DSC detection and screening. Our platform is a cost-effective tool that demonstrates robust performance given a heterogeneous dataset. However, additional validation studies are required to evaluate the model's generalizability across different populations and optimize its seamless integration into various types of clinical practice.
ABSTRACT
Thoracic disc herniations (TDHs) are very rare. While most common in the setting of trauma, other etiologies have been documented. Here, we present a case of spontaneous TDHs in the setting of tobacco abuse and coronavirus disease 2019 (COVID-19) causing acute paraplegia. We review spontaneous TDHs, associated risk factors, and the possible role of COVID-19 in the pathophysiology.
ABSTRACT
The utilization of three-dimensional (3D) models has been an important element of medical education. We demonstrate a three-dimensionally-printed (3DP) thoracic spine model for use in the teaching of freehand pedicle screw placement. Neurosurgical residents with varying years of experience practiced screw placement on these models. Residents were timed, and models were evaluated for medial and lateral breaches. Overall, this technical report describes the utility of 3D spine models in the training of thoracic pedicle screw placement. The tactile feedback from the 3D models was designed to represent both cortical and cancellous bones.
ABSTRACT
BACKGROUND: Vertebral artery injury (VAI) can be a devastating complication during cervical spine surgery. Although considered a rare occurrence overall, incidences of VAI have been reported in the ranges of 0.07% to 8%. Such injuries have the potential for catastrophic consequences, including blood loss, permanent morbid neurologic injury, and even death. The introduction of intraoperative navigation using either preoperative or intraoperative imaging has now been widely adopted in current practice so as to try and minimize adverse outcomes while giving real-time, dynamic information of the operative field. The use of the O-arm Surgical Imaging System during cervical spine surgery allows one to obtain high-resolution, accurate intraoperative imaging, and when used in concert with forms of intraoperative navigation, it can help with instrumentation and safety. However, patients undergoing cervical spine surgery do not routinely undergo preoperative vascular imaging, particularly with regard to anterior cervical or posterior high-cervical surgeries, where the incidence of VAI, in comparison with other cervical surgeries, has been reported to be the highest. METHODS: Here we present the use of intraoperative O-arm-based arteriography for integration with navigation for vertebral artery localization during C1 to C3 posterior instrumentation and fusion of an unstable C2 fracture in a 54-year-old man. RESULTS: The patient did not experience any intraoperative VAI and was subsequently discharged with no focal neurologic deficits. CONCLUSIONS: Detailed in our report is our protocol and procedure for obtaining and using intraoperative angiographic images. CLINICAL RELEVANCE: Case report detailing O arm for intraoperative identification of vertebral arteries during C1-C3 posterior instrumentation and fusion with pre-operative unilateral vertebral artery injury.
ABSTRACT
Spinal epidural angiolipoma is an uncommon finding; this case is presented to display the medical and legal implications of MRI scout imaging. In this case, a preceding period of ambiguous and non-focal symptoms led to an MRI of the lumbar spine without contrast with a scout image that captured a thoracic lesion. Review of the scout film led to a subsequent MRI of the thoracic spine with and without contrast that aided clinical decision making leading to surgical resection of the identified lesion and resolution of symptoms for this patient. The use of scout imaging has been described in the literature, but no concise agreement among physicians or professional medical societies exists regarding what utility, if any, may be obtained from the review of scout imaging. A discussion of medical legal implications of MRI scout imaging follows.
