Martínez, Christian

Background: Vertebral body defects pose a significant challenge in spinal reconstructive surgery. Compression fractures of the vertebral corpus are typically treated with vertebral augmentation procedures. There are significant risks associated with the introduction of foreign material in the spine, including infection and pseudarthrosis. Vascularized bone grafts (VBGs) have become a popular alternative for spinal reconstruction in the last decade thanks to their robust blood supply and autologous nature. VBGs have been described predominantly for the reconstruction of posterior vertebral defects. The objective of this study is to describe a novel procedure for the reconstruction of vertebral corpus defects in the lumbar and distal thoracic spine. Methods: This is a cadaveric anatomy study. The authors performed the novel surgical technique on an anonymized cadaveric donor in the prone position and recorded the procedure through clinical photography. Consent for the procedure and photography was obtained from the Center for Procedural Innovation. Results: A 5-cm-long posterior iliac crest VBG (IC-VBG) was successfully harvested on a pedicle of the quadratus lumborum muscle. The VBG was rotated and mobilized anteromedially to the vertebral corpus. In this donor, the IC-VBG could be inset with minimal tension anywhere along the vertebral bodies of the spine from the L5 to the T9 level. Conclusions: A rotated, anteromedially mobilized IC-VBG represents a novel technique for reconstruction of the anterior vertebral corpus of the lumbar and distal thoracic spine with a lower risk of infection and pseudarthrosis than reconstruction with foreign materials.

Background: Artificial intelligence (AI) is a promising tool to aid in diagnostic accuracy and patient communication. Prior literature has shown that ChatGPT answers medical questions and can accurately diagnose surgical conditions. The purpose of this study was to determine the accuracy of ChatGPT 4.0 in evaluating radiologic imaging of common orthopedic upper extremity bony pathologies, including identifying the imaging modality and diagnostic accuracy. Methods: Diagnostic imaging was sourced from an open-source radiology database for 6 common upper extremity bony pathologies: distal radius fracture (DRF), metacarpal fracture (MFX), carpometacarpal osteoarthritis (CMC), humerus fracture (HFX), scaphoid fracture (SFX), and scaphoid nonunion (SN). X-ray, computed tomography (CT), and magnetic resonance imaging (MRI) modalities were included. Fifty images were randomly selected from each pathology where possible. Images were uploaded to ChatGPT 4.0 and queried for imaging modality, laterality, and diagnosis. Each image query was completed in a new ChatGPT search tab. Multinomial linear regression was used to identify variations in ChatGPT’s diagnostic accuracy across imaging modalities and medical conditions. Results: Overall, ChatGPT provided a diagnosis for 52% of images, with accuracy ranging from 0% to 55%. Diagnostic accuracy was significantly lower for SFX and MFX relative to HFX. ChatGPT was significantly less likely to provide a diagnosis for MRI relative to CT. Diagnostic accuracy ranged from 0% to 40% with regard to imaging modality (x-ray, CT, MRI) though this difference was not statistically significant. Conclusions: ChatGPT’s accuracy varied significantly between conditions and imaging modalities, though its iterative learning capabilities suggest potential for future diagnostic utility within hand surgery.