HoloYolo: A proof‐of‐concept study for marker‐less surgical navigation of spinal rod implants with augmented reality and on‐device machine learning. Issue 1 (6th November 2020)

• Record Type: Journal Article
• Authors: von Atzigen, Marco; Liebmann, Florentin; Hoch, Armando; Bauer, David E.; Snedeker, Jess Gerrit; Farshad, Mazda; Fürnstahl, Philipp
• Is Part Of: International journal of medical robotics and computer assisted surgery. Volume 17:Issue 1(2021)
• Page Start: 1
• Page End: 10
• Publication Date: 2020-11-06
• Subjects: augmented reality -- machine learning -- object detection -- rod bending -- spinal fusion -- surgical navigation; Robotics in medicine -- Periodicals; Surgery -- Technological innovations -- Periodicals; Imaging systems in medicine -- Periodicals; 617.90285
• DOI: 10.1002/rcs.2184
• Languages: English
• ISSNs: 1478-5951

Abstract

Abstract: Background: Existing surgical navigation approaches of the rod bending procedure in spinal fusion rely on optical tracking systems that determine the location of placed pedicle screws using a hand‐held marker. Methods: We propose a novel, marker‐less surgical navigation proof‐of‐concept to bending rod implants. Our method combines augmented reality with on‐device machine learning to generate and display a virtual template of the optimal rod shape without touching the instrumented anatomy. Performance was evaluated on lumbosacral spine phantoms against a pointer‐based navigation benchmark approach and ground truth data obtained from computed tomography. Results: Our method achieved a mean error of 1.83 ± 1.10 mm compared to 1.87 ± 1.31 mm measured in the marker‐based approach, while only requiring 21.33 ± 8.80 s as opposed to 36.65 ± 7.49 s attained by the pointer‐based method. Conclusion: Our results suggests that the combination of augmented reality and machine learning has the potential to replace conventional pointer‐based navigation in the future.