TY - JOUR
T1 - Clinical Translation of the LevelCheck Decision Support Algorithm for Target Localization in Spine Surgery
AU - Manbachi, Amir
AU - De Silva, Tharindu
AU - Uneri, Ali
AU - Jacobson, Matthew
AU - Goerres, Joseph
AU - Ketcha, Michael
AU - Han, Runze
AU - Aygun, Nafi
AU - Thompson, David
AU - Ye, Xiaobu
AU - Vogt, Sebastian
AU - Kleinszig, Gerhard
AU - Molina, Camilo
AU - Iyer, Rajiv
AU - Garzon-Muvdi, Tomas
AU - Raber, Michael
AU - Groves, Mari
AU - Wolinsky, Jean Paul
AU - Siewerdsen, Jeffrey H.
N1 - Funding Information:
The work was supported by the National Institutes of Health (NIH R01-EB-017226) and academic-industry partnership with Siemens Healthineers (XP Division, Erlangen, Germany). The authors have publicly available intellectual property associated with this work, yet apart from paid employment by Siemens employees (Sebastian Vogt and Gerhard Kleinszig), the authors have no personal financial interest in the work reported in this paper.
Funding Information:
This work would have not been possible without the cooperation of Kelly Menon, Tangie Gaither-Bacon and Samantha Ernest as the Neurosurgical nursing team who supported the study in the operating rooms. Similarly, the support received from the radiology team is acknowledged?in particular: Rebecca Engberg, Jessica Enwright Wood, Charles Arterson, Joshua Shannon, Aris Thompson, and Lauryn Hancock. Finally, the authors would like to thank Ian Suk for his help and support. The work was supported by the National Institutes of Health (NIH R01-EB-017226) and academic-industry partnership with Siemens Healthineers (XP Division, Erlangen, Germany). The authors have publicly available intellectual property associated with this work, yet apart from paid employment by Siemens employees (Sebastian Vogt and Gerhard Kleinszig), the authors have no personal financial interest in the work reported in this paper. This research was conducted in accordance with the Institutional Review Board protocol NA_00078717, approved by Johns Hopkins Medical Institution: (Principal Investigator: Jean-Paul Wolinsky M.D.).
Publisher Copyright:
© 2018, Biomedical Engineering Society.
PY - 2018/10/15
Y1 - 2018/10/15
N2 - Recent work has yielded a method for automatic labeling of vertebrae in intraoperative radiographs as an assistant to manual level counting. The method, called LevelCheck, previously demonstrated promise in phantom studies and retrospective studies. This study aims to: (#1) Analyze the effect of LevelCheck on accuracy and confidence of localization in two modes: (a) Independent Check (labels displayed after the surgeon’s decision) and (b) Active Assistant (labels presented before the surgeon’s decision). (#2) Assess the feasibility and utility of LevelCheck in the operating room. Two studies were conducted: a laboratory study investigating these two workflow implementations in a simulated operating environment with 5 surgeons, reviewing 62 cases selected from a dataset of radiographs exhibiting a challenge to vertebral localization; and a clinical study involving 20 patients undergoing spine surgery. In Study #1, the median localization error without assistance was 30.4% (IQR = 5.2%) due to the challenging nature of the cases. LevelCheck reduced the median error to 2.4% for both the Independent Check and Active Assistant modes (p < 0.01). Surgeons found LevelCheck to increase confidence in 91% of cases. Study #2 demonstrated accuracy in all cases. The algorithm runtime varied from 17 to 72 s in its current implementation. The algorithm was shown to be feasible, accurate, and to improve confidence during surgery.
AB - Recent work has yielded a method for automatic labeling of vertebrae in intraoperative radiographs as an assistant to manual level counting. The method, called LevelCheck, previously demonstrated promise in phantom studies and retrospective studies. This study aims to: (#1) Analyze the effect of LevelCheck on accuracy and confidence of localization in two modes: (a) Independent Check (labels displayed after the surgeon’s decision) and (b) Active Assistant (labels presented before the surgeon’s decision). (#2) Assess the feasibility and utility of LevelCheck in the operating room. Two studies were conducted: a laboratory study investigating these two workflow implementations in a simulated operating environment with 5 surgeons, reviewing 62 cases selected from a dataset of radiographs exhibiting a challenge to vertebral localization; and a clinical study involving 20 patients undergoing spine surgery. In Study #1, the median localization error without assistance was 30.4% (IQR = 5.2%) due to the challenging nature of the cases. LevelCheck reduced the median error to 2.4% for both the Independent Check and Active Assistant modes (p < 0.01). Surgeons found LevelCheck to increase confidence in 91% of cases. Study #2 demonstrated accuracy in all cases. The algorithm runtime varied from 17 to 72 s in its current implementation. The algorithm was shown to be feasible, accurate, and to improve confidence during surgery.
KW - Clinical translation
KW - Image-guided surgery
KW - Intraoperative imaging
KW - LevelCheck
KW - Spine surgery
KW - Surgical workflow
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U2 - 10.1007/s10439-018-2099-2
DO - 10.1007/s10439-018-2099-2
M3 - Article
C2 - 30051244
AN - SCOPUS:85050761430
SN - 0090-6964
VL - 46
SP - 1548
EP - 1557
JO - Annals of biomedical engineering
JF - Annals of biomedical engineering
IS - 10
ER -