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Summary:. Demand for gender-affirming facial surgery is growing rapidly. Frontal sinus setback, one of the key procedures used in gender-affirming facial surgery, has a particularly high impact on gender perception. Mixed reality (MR) allows a user to view and virtually overlay three-dimensional imaging on the patient and interact with it in real time. We used the Medivis’s SurgicalAR system in conjunction with the Microsoft HoloLens Lucille2 (Microsoft). Computed tomography imaging was uploaded to SurgicalAR, and a three-dimensional (3D) hologram was projected onto the display of the HoloLens. The hologram was registered and matched to the patient, allowing the surgeon to view bony anatomy and underlying structures in real time on the patient. The surgeon was able to outline the patient’s frontal sinuses using the hologram as guidance. A 3D printed cutting guide was used for comparison. Negligible difference between the mixed reality-based outline and 3D-printed outline was seen. The process of loading the hologram and marking the frontal sinus outline lasted less than 10 minutes. The workflow and usage described here demonstrate significant promise for the use of mixed reality as imaging and surgical guidance technology in gender-affirming facial surgery.

Ultrasound imaging is a vital imaging tool in musculoskeletal medicine, with the number of publications on ultrasound-guided surgery increasing in recent years, especially in minimally invasive procedures of sports, foot and ankle, and hand surgery. However, ultrasound imaging has drawbacks, such as operator dependency and image obscurity. Artificial intelligence (AI) and deep learning (DL), a subset of AI, can address these issues. AI/DL can enhance screening practices for hip dysplasia and osteochondritis dissecans (OCD) of the humeral capitellum, improve diagnostic accuracy for carpal tunnel syndrome (CTS), and provide physicians with better prognostic prediction tools for patients with knee osteoarthritis. Building on these advancements, DL methods, including segmentation, detection, and localization of target tissues and medical instruments, also have the potential to allow physicians and surgeons to perform ultrasound-guided procedures more accurately and efficiently. This review summarizes recent advances in ultrasound-guided procedures for musculoskeletal diseases and provides a comprehensive overview of the utilization of AI/DL in ultrasound for musculoskeletal medicine, particularly focusing on ultrasound-guided surgery.

Background: Surgical scrubbing, gowning, and gloving is challenging for medical trainees to learn in the operating room environment. Currently, there are few reliable or valid tools to evaluate a trainee’s ability to scrub, gown and glove. The objective of this study is to test the reliability and validity of a checklist that evaluates the technique of surgical scrubbing, gowning and gloving (SGG). Methods: This Institutional Review Board-approved study recruited medical students, residents, and fellows from an academic, tertiary care institution. Trainees were stratified based upon prior surgical experience as novices, intermediates, or experts. Participants were instructed to scrub, gown and glove in a staged operating room while being video-recorded. Two blinded raters scored the videos according to the SGG checklist. Reliability was assessed using the intraclass correlation coefficient for total scores and Cohen’s kappa for item completion. The internal consistency and discriminant validity of the SGG checklist were assessed using Cronbach alpha and the Wilcoxon rank sum test, respectively. Results: 56 participants were recruited (18 novices, 19 intermediates, 19 experts). The intraclass correlation coefficient demonstrated excellent inter-rater reliability for the overall checklist (0.990), and the Cohen’s kappa ranged from 0.598 to 1.00. The checklist also had excellent internal consistency (Cronbach’s alpha 0.950). A significant difference in scores was observed between all groups (p < 0.001). Conclusion: This checklist demonstrates a high inter-rater reliability, discriminant validity, and internal consistency. It has the potential to enhance medical education curricula.