Study Design: Prospective cohort study. Objectives: In spine surgery, accurate screw guidance is critical to achieving satisfactory fixation. Augmented reality (AR) is a novel technology to assist in screw placement and has shown promising results in early studies. This study aims to provide our early experience evaluating safety and efficacy with an Food and Drug Administration-approved head-mounted (head-mounted device augmented reality (HMD-AR)) device. Methods: Consecutive adult patients undergoing AR-assisted thoracolumbar fusion between October 2020 and August 2021 with 2 -week follow-up were included. Preoperative, intraoperative, and postoperative data were collected to include demographics, complications, revision surgeries, and AR performance. Intraoperative 3D imaging was used to assess screw accuracy using the Gertzbein-Robbins (G-R) grading scale. Results: Thirty-two patients (40.6% male) were included with a total of 222 screws executed using HMD-AR. Intraoperatively, 4 (1.8%) were deemed misplaced and revised using AR or freehand. The remaining 218 (98.2%) screws were placed accurately. There were no intraoperative adverse events or complications, and AR was not abandoned in any case. Of the 208 AR-placed screws with 3D imaging confirmation, 97.1% were considered clinically accurate (91.8% Grade A, 5.3% Grade B). There were no early postoperative surgical complications or revision surgeries during the 2 -week follow-up. Conclusions: This early experience study reports an overall G-R accuracy of 97.1% across 218 AR-guided screws with no intra or early postoperative complications. This shows that HMD-AR-assisted spine surgery is a safe and accurate tool for pedicle, cortical, and pelvic fixation. Larger studies are needed to continue to support this compelling evolution in spine surgery.

Since the inception of modern medicine, surgical trainees have benefitted from coaching and feedback from senior surgeons. In the high-demand operating room environment, granular feedback is often limited by time constraints. Artificial intelligence (AI) represents a novel frontier in surgical education in that it may be able to provide reliable, objective, and comprehensive feedback in an automated manner. While prior studies have given broad overviews of AI in surgical training, there has been no study to our knowledge that has codified the myriad applications and offered guidance for which applications best suit trainees at various levels of surgical education. We aim to describe four key domains under which surgical training AI applications can be classified. We define which applications would best suit junior, intermediate, and experienced surgeons by matching their educational needs. The four domains include: enhanced simulation (realistic and safe training environment), real time visual cues (guidance

Focused ultrasound (FUS) is a minimally invasive treatment that utilizes high-energy ultrasound waves to thermally ablate tissue. Magnetic resonance imaging (MRI) guidance may be combined with FUS (MRgFUS) to increase its accuracy and has been proposed for lung tumor ablation/debulking. However, the lungs are predominantly filled with air, which attenuates the strength of the FUS beam. This investigation aimed to test the feasibility of a new approach using an intentional lung collapse to reduce the amount of air inside the lung and a controlled hydrothorax to create an acoustic window for transcutaneous MRgFUS lung ablation. Eleven pigs had one lung mechanically ventilated while the other lung underwent a controlled collapse and subsequent hydrothorax of that hemisphere. The MRgFUS lung ablations were then conducted via the intercostal space. All the animals recovered well and remained healthy in the week following the FUS treatment. The location and size of the ablations were confirmed one week post-treatment via MRI, necropsy, and histological analysis. The animals had almost no side effects and the skin burns were completely eliminated after the first two animal studies, following technique refinement. This study introduces a novel methodology of MRgFUS that can be used to treat deep lung parenchyma in a safe and viable manner.