Man versus machine: Automatic pedicle screw planning using registration‐based techniques compared with manual screw planning for thoracolumbar fusion surgeries

International Journal of Medical Robotics and Computer Assisted Surgery, vol. 20, pp. e2570

Abstract

OBJECTIVE: This study evaluates the precision of a commercially available spine planning software in automatic spine labelling and screw-trajectory proposal. METHODS: The software uses automatic segmentation and registration of the vertebra to generate screw proposals. 877 trajectories were compared. Four neurosurgeons assessed suggested trajectories, performed corrections, and manually planned pedicle screws. Additionally, automatic identification/labelling was evaluated. RESULTS: Automatic labelling was correct in 89% of the cases. 92.9% of automatically planned trajectories were in accordance with G&R grade A + B. Automatic mode reduced the time spent planning screw trajectories by 7 s per screw to 20 s per vertebra. Manual mode yielded differences in screw-length between surgeons (largest distribution peak: 5 mm), automatic in contrast at 0 mm. The size of suggested pedicle screws was significantly smaller (largest peaks in difference between 0.5 and 3 mm) than the surgeon's choice. CONCLUSION: Automatic identification of vertebrae works in most cases and suggested pedicle screw trajectories are acceptable. So far, it does not substitute for an experienced surgeon's assessment.

Authors 6

1. Ulf Bertram corresponding Aachen Department of Neurosurgery

   RWTH Aachen University

   Affiliation as printed

   Department of Neurosurgery RWTH Aachen University Aachen Germany

   Department of Neurosurgery, RWTH Aachen University, Aachen, Germany

2. István Kövesházi

   LMU Klinikum · Ludwig-Maximilians-Universität München

   Affiliation as printed

   Department of Orthopedics and Trauma Surgery Musculoskeletal University Center Munich (MUM) University Hospital LMU Munich Munich Germany

   M3i Industry‐in‐Clinic‐Platform GmbH Munich Germany

   Department of Orthopedics and Trauma Surgery, Musculoskeletal University Center Munich (MUM), University Hospital, LMU Munich, Munich, Germany

   M3i Industry-in-Clinic-Platform GmbH, Munich, Germany

3. Simon Weidert

   LMU Klinikum · Ludwig-Maximilians-Universität München

   Affiliation as printed

   Department of Orthopedics and Trauma Surgery Musculoskeletal University Center Munich (MUM) University Hospital LMU Munich Munich Germany

   M3i Industry‐in‐Clinic‐Platform GmbH Munich Germany

   Department of Orthopedics and Trauma Surgery, Musculoskeletal University Center Munich (MUM), University Hospital, LMU Munich, Munich, Germany

   M3i Industry-in-Clinic-Platform GmbH, Munich, Germany

4. Tobias Philip Schmidt Aachen Department of Neurosurgery

   RWTH Aachen University

   Affiliation as printed

   Department of Neurosurgery RWTH Aachen University Aachen Germany

   Department of Neurosurgery, RWTH Aachen University, Aachen, Germany

5. Christian Blume Aachen Department of Neurosurgery

   RWTH Aachen University

   Affiliation as printed

   Department of Neurosurgery RWTH Aachen University Aachen Germany

   Department of Neurosurgery, RWTH Aachen University, Aachen, Germany

6. Christian‐Andreas Müller Aachen Department of Neurosurgery

   RWTH Aachen University

   Affiliation as printed

   Department of Neurosurgery RWTH Aachen University Aachen Germany

   Department of Neurosurgery, RWTH Aachen University, Aachen, Germany