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

Author:

Bertram Ulf1ORCID,Köveshazi Istvan23,Michaelis Monika3,Weidert Simon23,Schmidt Tobias Philip1,Blume Christian1,Zastrow Felix Swamy v.34,Müller Christian‐Andreas1,Szabo Szilard3

Affiliation:

1. Department of Neurosurgery RWTH Aachen University Aachen Germany

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

3. M3i Industry‐in‐Clinic‐Platform GmbH Munich Germany

4. Department of Neurology University Hospital LMU Munich Munich Germany

Abstract

AbstractObjectiveThis study evaluates the precision of a commercially available spine planning software in automatic spine labelling and screw‐trajectory proposal.MethodsThe 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.ResultsAutomatic 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.ConclusionAutomatic 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.

Funder

Bundesministerium für Bildung und Forschung

Publisher

Wiley

Subject

Computer Science Applications,Biophysics,Surgery

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A review: artificial intelligence in image-guided spinal surgery;Expert Review of Medical Devices;2024-08-02

2. Automated Patient-Specific C1-C2 Posterior Cervical Fusion Screw Trajectory Planning using 3D Deep Learning;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

3. GUI-Based Pedicle Screw Planning on Fluoroscopic Images Utilizing Vertebral Segmentation;2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA);2024-06-26

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