Predicting adjacent segment disease with deep learning on pre-operative cervical MRI for patients undergoing surgery for cervical radiculopathy

Brain and Spine, vol. 1, pp. 100405

Abstract

not differ between the two groups, more screws had to be revised intraoperatively in the CBCT group (ORCT: 98, 2.8% vs. CBCT: 128, 4.0%; p¼0.0081).The median time of patients inside the OR (Entry -Exit) was significantly shorter for the ORCT group (ORCT: median, [95% CI] 237.5, [247.8,264.3] min, CBCT: 267, [274.4,291.5] min; p<0.0001) based on shorter times for Positioning -Incision (ORCT: 17, [18.1, 19.9] min, CBCT: 32, [32.2, 35.5] min; p<0.0001) and Suture -Exit (ORCT: 22, [23.6, 26.1] min, CBCT: 25.5, [27.5, 30.7] min; p<0.0001).Conclusion: The present results show that the choice of assistive technology for navigated pedicle screw placement has significant impact on standard spine procedures even in a high-volume spine center.Particularly with regard to costeffectiveness and the duration of surgeries, the shorter time needed for preparation and de-positioning in the ORCT group made the main difference, while the quality of accuracy was even higher.

Authors 10

1. Caroline M.W. Goedmakers Aachen

   Brigham and Women's Hospital · Harvard University · Leiden University Medical Center

   Affiliation as printed

   Computational Neurosciences Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Department of Neurosurgery, Boston, United States

   Leiden University Medical Center, Neurosurgery, Leiden, Netherlands

2. Asad Mehmood Lak

   Brigham and Women's Hospital · Harvard University

   Affiliation as printed

   Computational Neurosciences Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Department of Neurosurgery, Boston, United States

3. Akiro H. Duey

   Brigham and Women's Hospital · Harvard University

   Affiliation as printed

   Computational Neurosciences Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Department of Neurosurgery, Boston, United States

4. Alexander W. Senko

   Brigham and Women's Hospital · Harvard University

   Affiliation as printed

   Computational Neurosciences Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Department of Neurosurgery, Boston, United States

5. Omar M. Arnaout

   Brigham and Women's Hospital · Harvard University

   Affiliation as printed

   Computational Neurosciences Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Department of Neurosurgery, Boston, United States

6. Michael W. Groff

   Brigham and Women's Hospital · Harvard University

   Affiliation as printed

   Computational Neurosciences Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Department of Neurosurgery, Boston, United States

7. Timothy Richard Smith

   Brigham and Women's Hospital · Harvard University

   Affiliation as printed

   Computational Neurosciences Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Department of Neurosurgery, Boston, United States

8. Carmen L. A. Vleggeert-Lankamp Aachen

   Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center, Neurosurgery, Leiden, Netherlands

9. Hasan A. Zaidi

   Brigham and Women's Hospital · Harvard University

   Affiliation as printed

   Computational Neurosciences Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Department of Neurosurgery, Boston, United States

10. Alessandro Boaro

    Brigham and Women's Hospital · Harvard University

    Affiliation as printed

    Computational Neurosciences Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Department of Neurosurgery, Boston, United States