Journal of Shandong University (Health Sciences) ›› 2023, Vol. 61 ›› Issue (3): 80-89.doi: 10.6040/j.issn.1671-7554.0.2022.1280

• 临床医学 • Previous Articles    

Preliminary exploration of automatic planning of lumbar pedicle screws based on cone-beam CT in 130 cases

LIU Yajun1,2, YUAN Qiang1, WU Jingye1, HAN Xiaoguang1, LANG Zhao1, ZHANG Yong3   

  1. 1. Department of Spine Surgery, Beijing Jishuitan Hospital, Beijing 100035, China;
    2. Beijing Research Institute of Traumatology and Orthopaedics, Beijing 100035, China;
    3. Tianji Laboratory, Beijing Tinavi Medical Technologies Co., Ltd, Beijing 100192, China
  • Published:2023-03-24

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Abstract: Objective To develop an automatic pedicle screw planning system based on cone-beam CT(CBCT)images using deep learning technology, and to evaluate its efficiency and planning efficiency. Methods The CBCT and screw planning data of 130 cases who underwent robot-assisted lumbar pedicle screw fixation from a single academic institution during Jan. 2017 and Dec. 2019 were retrospectively analyzed, including 100 cases for network training for spinal segmentation and automatic planning, and 30 cases for validation. The segmentation results were evaluated using Dice coefficient and qualitative validation. The pedicle screw placement was evaluated using the Ravi and Zdichavsky grading systems. The viocation of superior facet joint was evaluated with Babus method. Interobserver reliability was assessed with Kappa statistics. Results In the quantitative assessment of segmentation in 10 cases, the Dice value was 0.99. In the qualitative evaluation of segmentation in 30 cases, the average acceptance rate of the segmentation results by experts was 94.9%, and the average satisfaction rate was 80.5%. In total, the automatic placed 300 pedicle screws between the L1 and L5 spinal levels, including 300(100%)Zdichavsky 1A, 298(99.0%)Ravi 1, and 2(1.0%)Ravi 2(<2 mm breech), both of which were laterally deviated from the cortex. Of the 300 screws, 294(98%)were Grade 0, 6(2%, all L5)of which had superior facet joint violation, including 3(1%)Grade 1, 2(0.67%)Grade 2, and 1(0.33%)Grade 3. Kappa statistics showed excellent overall agreement between raters(K=0.71-0.94; 90%-96% agreement). In running speed evaluation, the average time required for automatic segmentation was 3.52 seconds, the time required for feature point extraction was 3.81 seconds, and the total average running time was 7.33 seconds. Conclusion The automatic planning system of pedicle screws based on CBCT images shows excellent automatic segmentation and screw planning, which has the potential to aid surgeons in screw planning and improve the navigation workflow of spinal surgical robots.

Key words: Pedicle screws, Deep learning, Cone-beam CT, Automatic planning, Image segmentation, Spinal surgical robot

CLC Number: 

  • R681.5
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