HoloYolo: A proof-of-concept study for marker-less surgical navigation of spinal rod implants with augmented reality and on-device machine learning

Int J Med Robot. 2021 Feb;17(1):1-10. doi: 10.1002/rcs.2184. Epub 2020 Nov 6.


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.

Keywords: augmented reality; machine learning; object detection; rod bending; spinal fusion; surgical navigation.

MeSH terms

  • Augmented Reality*
  • Humans
  • Machine Learning
  • Pedicle Screws*
  • Spine / diagnostic imaging
  • Spine / surgery
  • Surgery, Computer-Assisted*