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Objective. Holographic mixed reality (HMR) allows for the superimposition of computer-generated virtual objects onto the operator’s view of the world. Innovative solutions can be developed to enable the use of this technology during surgery. The authors developed and iteratively optimized a pipeline to construct, visualize, and register intraoperative holographic models of patient landmarks during spinal fusion surgery. Methods. The study was carried out in two phases. In phase 1, the custom intraoperative pipeline to generate patient-specific holographic models was developed over 7 patients. In phase 2, registration accuracy was optimized iteratively for 6 patients in a real-time operative setting. Results. In phase 1, an intraoperative pipeline was successfully employed to generate and deploy patient-specific holographic models. In phase 2, the registration error with the native hand-gesture registration was 20.2 ± 10.8 mm (n = 7 test points). Custom controller-based registration significantly reduced the mean registration error to 4.18 ± 2.83 mm (n = 24 test points, P < .01). Accuracy improved over time (B = −.69, P < .0001) with the final patient achieving a registration error of 2.30 ± .58 mm. Across both phases, the average model generation time was 18.0 ± 6.1 minutes (n = 6) for isolated spinal hardware and 33.8 ± 8.6 minutes (n = 6) for spinal anatomy. Conclusions. A custom pipeline is described for the generation of intraoperative 3D holographic models during spine surgery. Registration accuracy dramatically improved with iterative optimization of the pipeline and technique. While significant improvements and advancements need to be made to enable clinical utility, HMR demonstrates significant potential as the next frontier of intraoperative visualization.

Background The emergence of SARS-CoV-2/2019 novel coronavirus (COVID19) has created a global pandemic with no approved treatments or vaccines. Repurposing existing drugs and rapidly launching clinical trials present the greatest near-term opportunities for mitigating COVID19s impact. Many treatments have already been administered to COVID19 patients but have not been systematically evaluated. We performed a systematic literature review to identify and assess all treatments reported to be administered to COVID19 patients. Methods We searched PubMed, BioRxiv, MedRxiv, and ChinaXiv for articles reporting treatments for COVID19 patients published between Dec 1, 2019 and Mar 27, 2020. All studies reporting treatments were included. Data for each paper were manually extracted by two independent extractors. Outcomes included the duration from drug administration to clinically-meaningful response (complete symptom resolution or hospital discharge). Data were analysed descriptively. Results We identified 2,706 articles from single patient case reports to single-centre interventional studies. Of these, 155 studies met inclusion criteria, comprising 9,152 patients from 14 different countries. The cohort was 45.4% female and 98.3% hospitalised, and mean (SD) age was 44.4 years (SD 21.0). The most frequently administered drug classes were antivirals, antibiotics, and corticosteroids, and of the 115 reported drugs, the most frequently administered was combination lopinavir/ritonavir, which was associated with a response time of 11.7 (1.09) days. Discussion A large number of treatments have been administered to the first 9,152 reported cases of COVID19. Further work is needed to prioritise drugs for investigation in well-controlled clinical trials and treatment protocols. Funding None.